U.S. DEPARTMENT OF THE INTERIOR 

U.S. GEOLOGICAL SURVEY

Experiments in In Situ Fish Recognition Systems Using 

Fish Spectral and Spatial Signatures

by

'Charles Bendall, 

U.S. Department of the Navy, Space and Naval Warfare Systems Center San Diego

2Steve Hiebert, 

U.S. Bureau of Reclamation, Denver Technical Service Center, and

2Gordon Mueller, 

U.S. Geological Survey, Midcontinent Ecological Science Center.

Open File Report 99-104

This report is preliminary and has not been reviewed for conformity with U.S. Geological 

Survey editorial standards (or with the North American Stratigraphic Code). Any use of 

trade, product or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

2 1 Electro-Optics Branch 743, San Diego, CA 92152-6241 

2P.O. Box 25007 (D-8220), Denver, Colorado 80225
TABLE OF CONTENTS

Page

EXECUTIVE SUMMARY.................................................................................. iiii

INTRODUCTION................................................................................................ 1

METHODS........................................................................................................... 2

Image Calibration..................................................................................... 5

Calibration Error....................................................................................... 7

Fish Signatures......................................................................................... 9

Morphological Signatures........................................................................ 10

Analysis Procedures................................................................................. 11

RESULTS and DISCUSSIONS........................................................................... 14

Spectral Signatures................................................................................... 14

Moments.................................................................................................. 15

Moments and Morphological Signatures................................................. 18

Differences Between Centers................................................................... 18

Normalized Moment of Color.................................................................. 20

Fourier Transform Spatial Signatures...................................................... 20

Fourier Transform Signature Analysis Procedure.................................... 27

Fourier Transform Signatures.................................................................... 27

Histograms................................................................................................. 29

Signature DataBase................................................................................... 30

CONCLUSIONS.................................................................................................... 33

RECOMMENDATIONS....................................................................................... 34

ACKNOWLEDGMENTS...................................................................................... 35

LITERATURE CITED........................................................................................... 35

APPENDIX

Supporting Figures..................................................................................... 37

TABLE 

Number Page

1. Summary of spectral and spatial fish signatures from Test 1 & 2.. 13

2. Summary offish identification based on spectral and spatial

signatures......................................................................................... 31

3. Summarized matrix of differentiated speciation based on

spectral and/or spatial signatures..................................................... 34
FIGURE

Number Page

1. Comparison of spectral (high uniform reflectivity of one 

color) and spacial (reflectivities of different colors varying 

spatially like spots and stripes) signatures of four marine 

fish species.................................................................................... 3

2. Digitized image of a salmon inside the photographic holding 

cell with 5 round reflectant standards shown in the 

background..................................................................................... 5

3. Image calibration and corrections for lighting and nonuniforrn 

reflectivity...................................................................................

4. Illustration of the interpolation technique used to standardize 

background reflectance.............................................................

5. Comparison of diffused light reflection and preferential

(mirror) reflection............................................................................. 10

6. Illustration of the masking technique that isolated fish from

the surrounding background area...................................................... 11

7. Examples of different spatial patterns and the use of center of

mass and moment of inertia to quantify measures of differences...... 16

8. Illustration of the different body to length proportions of small

American shad and chinook salmon fry............................................ 18

9. Illustration of a bar pattern and how a Fourier transform power 

spectrum displays the vertical patterns to frequency bands 

along the Y axis................................................................................. 22

10. Illustration of a bar pattern and sine wave showing how a Fourier 

transform power spectrum displays the vertical patterns to 

frequency bands along the Y axis....................................................... 23

11. Illustration of a less intense bar pattern (Figure 9) and its Fourier

interpretation in frequency band......................................................... 24

12. Illustration of wide bands and the different way the Fourier

transform displays it from Figure 9................................................... 25

11
13. Illustration of a gradient bar pattern and how the Fourier

transform power spectrum displays that on the Y frequency 

axis.................................................................................................... 26

14. A Fourier transform power spectrum for striped bass...................... 29

15. Comparison of image histograms of fry and j ack chinook salmon... 30

in
EXECUTIVE SUMMARY

The objective of this study was to determine if inter-species spectral or spatial variations 

could be used to develop an automated fish identification system. Comparative tests 

were conducted using twelve fish species, (chinook salmon, striped bass, American shad, 

rainbow trout, northern pikeminnow, Sacramento sucker, channel catfish, hardhead, white 

catfish and white sturgeon) common to the Sacramento River, California. Experiments 

were performed under optimum laboratory conditions at the Bureau of Reclamation's 

Fish Research Facilities located near Red Bluff Diversion Dam, Red Bluff, California.

Spectral reflectance information was gathered from subject fish in the visible 

wavelengths range of 400 to 700 nm, using a CCD camera equipped with narrow band 

pass filters. Images were captured, digitized and stored on computer.

The twelve fish species could be divided into three broad (high, medium, and low) 

reflectivity categories, however, additional independent signature data was needed for in 

situ specific identification. Additional signatures were developed using basic 

morphometrics and spatial reflectivities (i.e., spots, stripes, color and reflectance gradient 

patterns). Morphometric measurements included fish size, pixel number within a mask, 

body length and depth ratios, and calculations of color moments. Spatial signatures 

showing promise either singly or in combination for species identification were: moment 

of color (quantified color gradient changes across the length or depth of the fish), center 

of color (quantified head and tail colors differing from the central fishes body), Fourier 

transformation signature of line profiles, and histogram signatures (pixel intensitynumber distributions) across the entire fish.

These experiments suggest that the technology already exists for an automated, in-situ 

fish identification system to work under laboratory conditions. Graphic analysis of 

morphometrics, spectral and spatial signature curves suggested there was differentiation 

between 10 of the 12 species (84%). The two not discernable were the northern 

pikeminnow and channel catfish. However, additional research may reveal signatures 

that could also differentiate these species.

Future spectral and spatial signature work should focus on three topics:

* Application of this data base of signatures and analysis techniques to an in-situ, 

field environment. Testing is needed to demonstrate the application of this technology to 

field conditions and determine the applicability of field generated images with high 

resolution laboratory images.

*More signature validation and refinements are needed with all species. 

Additional signatures are needed to distinguish fish species within each broad reflective 

category as well as confirm the category identification.

* Develop software for fish counting and identification for a computer vision 

system. This should include investigations of electro-optic technologies and optical 

water properties appropriate for an in-situ system.

mi
INTRODUCTION

Fishery managers have long searched for a method or technology that would provide 

rapid and accurate information regarding the movement or migrations of fish through 

river corridors. Hatchery released stocks can be implanted with magnetic tags that can 

be detected when fish pass through sensor arrays. Unfortunately, our ability to monitor 

and enumerate "wild" populations of migrants is extremely limited. Present technology 

can be used to detect fish, and in some cases identify fish size as migrants pass through 

confined fish weirs or ladders. However, actual speciation requires on site observers 

and,or photography that can be analyzed later with precise morphometric analysis. To 

our knowledge, no automated fish identification system currently exists that provides 

accurate, in-situ fish recognition information. The goal of this study was to test the 

feasibility and initial data collection for an optical system that is accurate, non-lethal and 

adaptable to an in-situ environment.

Morphometric characteristics have been used successfully as a nonlethal method of 

determining smoltification levels for juvenile spring chinook salmon and steelhead 

(Beeman et al. 1995, Hanes et al. 1995). These authors suggested spectrography might be 

used as a smoltification index. Morphometric characters have been used successfully to 

discriminate between the Gila species (Douglas et al. 1998). Video tape recordings have 

been used for monitoring fish passage at nearly all the Columbia and Snake River dams 

and computer image techniques have been developed to remove non-fish images from 

recorded video tape (Hatch et al. 1998)

This study is a cooperative venture of the US Geological Survey, Bureau of Reclamation, 

and Department of the Navy to determine if target recognition technology can be applied 

to fish recognition systems. Recent advances in computer processing power, data 

storage, camera electronics, has brought machine vision recognition systems closer to 

reality. Provided the resources, we feel a system could be developed and installed at 

an underwater diversion or impoundment structures that could notify managers in-situ of 

the occupance and numbers of specific fish species. Such a system could prove vital 

information in the management and recovery of endemic fish stocks in conjunction with 

water management objectives.

Our work focussed on developing a fish signature database for the purpose of species 

identification. A fish signature is a distinctive set of both spectral and spatial qualities 

which is uiquely characteristic to only one species. Experiments focused on measuring 

and distinguishing differences in spectral and spatial variations of major fish species 

found in the Sacramento and San Joaquin Rivers. Study fish were readily available from 

the Tracy Fish Salvage Facility (Tracy, California) and the resulting system could be 

tested using videos of fish passing through fish ladders at Red Bluff Diversion Dam (Red 

Bluff, California-RBDD). These experiments were intended to be the first step in a series 

designed to develop and test signature recognition techniques. This report presents 

preliminary results of the spectral and spatial signature determination process and the 

mathematical tests used to describe differences in shape and color distributions.

1
Inter-species spectral variations is one possible signature that is compatible with machine 

vision systems. Spectral signatures are obvious between some tropical fish species and 

can occur in various forms. One fish might be uniformly brighter for all colors or 

uniformly brighter in a particular color band (spectral signature). There can also be 

distinctive color patterns, such as stripes or spots, that provide another color signature 

type (spatial spectral signature) (Figure 1). While these are good examples, the spectral 

and spatial variations of Sacramento River species are much less obvious to the human 

eye, but, never the less are discernable.

Color and Reflectivity

The human eye is sensitive to light in the visible spectral region which includes 

wavelengths ranging from 400 to 700 nanometers (10'9 meters). We refer to a unique 

variation in wavelength reflectivity as a spectral signature. An object's color is 

determined by its reflectivity in this wavelength. Fish can also have distinctive 

reflectivities in particular spectral (color) regions. The fish in Figure 1A or IB have a 

high reflectivity in the red or blue spectral region. Fish with distinctive color patterns 

have reflectivities that vary spatially across the fish. Figure 1C and ID are two examples 

offish with spatial reflectivity distributions.

To measure fish color signatures, it is necessary to measure the fish's reflectivity 

throughout the visible spectral region. The remainder of this report discusses how these 

measurements were performed and the results of the analysis.

METHODS AND APPARATUS

Fish were captured from the wild and photographed to measure their spectral and spatial 

signatures in specialized photographic tanks. Originally 7 fish species (Chinook salmon, 

striped bass, American shad, rainbow trout, northern pikeminnow, Sacramento sucker, 

and channel catfish) were chosen for the test. Three additional species (hardhead, white 

catfish, and white sturgeon) were later added. Fish were obtained from the Sacramento 

River by screw traps and electrofishing and from the Tracy Fish Collection Facility 

located in the Sacramento/San Joaquin Delta.
FIGURE 1 A: Fish with red spectral 

signature

FIGURE IB: Fish with blue spectral 

signataure

FIGURE 1C: Fish with spatial signature 

(spots)

FIGURE ID: Fish with spatial 

signature (stripes)

Figure 1. Comparison of spectral (high uniform reflectivity of one color) and spatial 

( reflectivities of different colors varying spatially like spots and stripes) 

signatures of four marine fish species.
Tests were conducted in the Bureau of Reclamation's Fish Research Facility located near 

Red Bluff, California. Fish were brought to the facility just prior to the tests and held for 

a maximum of 4 days. Spectral imagery took approximately 30 to 100 minutes. At the 

conclusion, study fish were returned unharmed to the Sacramento River.

Test fish were placed in aO.8X1.5m diameter fiberglass tank that had a glass panel on 

one side. Behind the glass panel was an internal partition made of white Plexiglass that 

confined the fish next to the glass. The width (10-24 cm) between the glass and Plexiglas 

sheet could be adjusted to accommodate different sized fish while restricting their 

movement. A second, horizontal partition helped center the fish within the confinement. 

The tank was filled with clear well water and maintained at a constant 16 °C. Larger 

salmon and shad had to be slightly anesthetized with CO2 gas during the tests.

A charged coupled device (CCD) TV camera equipped with 16 narrow spectral bandpass 

filters was used to capture spectral and spatial fish signatures. The filters had center 

wavelengths of 400, 420, 440, 460, 480, 500, 520, 540, 560, 580, 600, 620, 640, 660, 

680, and 700 nanometers (nm) (respectively the visible violet, blue, green, yellow, 

orange, and red spectrum). The filters had a bandpass of 15 nm except for the 700 nm 

filter which had a 8 nm bandpass. In addition to the narrow spectral filters, we also used 

broadband red, blue and green filters similar to those used in color CCD cameras. The 

camera was set on a tripod, perpendicular (1.5m) from the viewing chamber.

The photographic chamber was illuminated with fluorescent lights that were positioned to 

reduce glare. A spectral image of a fish was obtained by placing a spectral bandpass filter 

in front of the CCD camera lens, capturing a frame of video with a frame grabber, 

digitizing the image and storing the digitized image on the computer. Next, a second 

filter was placed in front of the lens and a second image digitized. This procedure was 

repeated until images were obtained with each spectral filter. At least 19 images were 

taken of each fish.

Figure 2 provides an example of a digitized image, showing a fish and reflectance 

standards. Such images were digitized into a matrix consisting of 640 x 480 elements or 

pixels. Each pixel was comprised of 8 bits and could have ranged between 0 and 1 

depending on the intensity of light incident on the pixel. Pixels with a value of 1 are 

saturated (i.e., increasing the light intensity does not increase the pixel value). Saturated 

pixels are undesirable and we used special neutral density (ND) filters to limit the number 

of saturated pixels in each image.
Figure 2. Digitized image of a salmon in the photographic cell with the 5 round 

reflectance standards shown in the background.

Image Calibration

The camera measured the light intensity reflected from the fish within the bandpass of the 

filter. The intensity reflected from the fish into the camera was proportional to the 

product of the light intensity incident on the fish and the fish's reflectivity (Equation 1)

reflectedW- Equation 1

where IrefiectedW was the reflected intensity, IjnCjdentW was the incident intensity and 

was the reflectivity at wavelength X.

The term G was used to represent a number of factors that determine the amount of the 

reflected light that actually was imaged onto the cameras CCD array. Included in G were 

such factors as the size of the lens, the distance between the fish and the camera, and the 

transmission of the water. We have assumed that G was the same for all fish and was 

independent of wavelength. This is not strictly true but, simplifies the calculations. The 

issue of specular reflectivity that will be discussed later is an example where the 

assumptions regarding G are violated. The reflectivity can be determined from the ratio 

of the reflected and incident intensities. This task was not as easy as it might seem 

because the lighting was not uniform throughout the tank nor was the response of the 

camera uniform. In order to utilize Equation 1 it was necessary to calibrate the camera.

The first step in the calibration procedure was to correct for nonuniform lighting and the 

unknown factors of G. This was accomplished by placing a white background plate with 

uniform reflectivity in the tank. A digitized computer image of the plate, such as that 

shown is Figure 3 A, was obtained. The nonuniformity of the lighting and camera can be 

seen from the horizontal line profile shown in Figure 3B. If the camera response and 

lighting was uniform, the curve in Figure 3B should be a straight line. From the line 

profile it is evident that the combined effects of lighting, geometric factors, and camera
CALIBRATION BACKGROUND

1.5

1

0.5

0

0 200 400 600 

NONUNIFORMITY CORRECTION

1

0.8 

0.6 

0.4 

0.2

0

0

0

0

200 400 

LINE PROFILE

600

0.5 1 

PIXEL VALUE

Figure 3. Image calibration and corrections for lighting and nonuniform reflectivity
response results in a pixel values ranging from 0.1 to 1.0. The plate has a uniform 

reflectivity but substituting into Equation 1 without correcting for the light and camera 

nonuniformities would mistakenly yield nonuniform reflectance values for the plate.

The easiest way to correct for the nonuniformities was to create a correction image by 

replacing each pixel value in the original image by its reciprocal value (1/pixel value). 

Multiplication of the original image by the correction image produces a uniform response 

as indicated by the line profile in Figure 3C. The nonuniformity correction was valid 

only for the lighting and camera settings from which it was measured. It was important 

not to move the camera or the lights, adjust the iris on the lens or change the camera's 

internal gain settings while acquiring the fish images. The camera's automatic gain 

control was disabled and the iris fixed at the full open setting. The iris was normally used 

to control the intensity of the light reaching the camera's focal plane. With the iris fixed 

in the full open position many of the images had large numbers of saturated pixels. 

Neutral density filters were placed in front of the camera lens to limit the number of 

saturated pixels.

The next step in the calibration procedure was to convert pixel values to reflectivity. To 

accomplish this, a set of five reflectance standards were placed in the tank with 

reflectance values of 2%, 9%, 47%, 74% and 99%. The standards appeared as five circles 

at the top of in each digitized computer fish image. For each image, a linear least square 

fit was performed on the reflectance standards to provide a calibration from pixel value to 

reflectivity. An example of a curve is shown in Figure 3D. The horizontal axis 

corresponds to the pixel value (0 to 1) and the vertical axis is the corresponding 

reflectance values of the five standards. The straight line represents the result of the 

linear least square fit and gives the conversion from pixel value to reflectivity. The 

equation for the line show in Figure 3D is given by:

reflectivity = slope * pixel value + y intercept Equation 2

The reflectivity of each pixel was calculated by inserting the pixel value into Equation 2. 

Once the calibrated procedure had been completed, the spectral and spatial signatures 

could be extracted from the image. A new nonuniformity correction was made whenever 

the experimental setup was modified. This occurred whenever lights or camera were 

moved or lenses changed on the camera. The nonuniformity correction was assumed to 

be valid independent of the filter wavelength and fish. The conversion from pixel value 

to reflectivity was calculated for each digitized fish image.

Sources of Error in Calibration

Partially valid assumptions and systematic errors were inevitable during the calibration 

process and these measurements were no exception. Several of the most important are 

discussed below.
The frame grabber introduced a digitization offset into the data. Images acquired with the 

very low light levels (room darkened and the lens cap placed on the camera) did not result 

in pixel values of zero. Each pixel always had a small but significant value. It was 

necessary to remove this value before calibrating the image. For the second set of tests 

(Test 2-September 1995) the digitization offset was obtained from the reflection standard 

holder shown in Figure 2. This was painted black and had a very small reflectivity which 

we assumed to be zero. We located the darkest area on the holder, calculated the mean 

pixel value within the area and used this as the digitization offset. The digitization offset 

was subtracted from the image prior to calibration. In the initial tests in June, the 

standard holder was made of wood and did not provide a good measure of the digitization 

offset. Instead, we tried to locate the darkest area in the image and used the mean pixel 

value for this area as the digitization offset. Inaccuracies in the digitization offset would 

have had the greatest impact on those fish with lowest reflectivities.

For the nonuniformity correction, we had a 12 inch x 12 inch reflection standard with a 

reflectivity of approximately 70%. The tank cell was approximately 80-cm-wide so it 

required three images with the reflection standard on the left, center and right portions of 

the tank. The plan was to combine the images to create a uniform background. For Test 

1 this process did not always produce a seamless uniform background. In these instances 

the Plexiglas sheet used to restrict the fish movement was used as the uniform 

background. The Plexiglas appeared to be uniform but, was probably not as uniform as 

the reflection standard. In Test 2 we unknowingly saturated a large portion of center 

image of the reflection standard. We again used the Plexiglas background as our uniform 

standard but, the calibration procedure was more complicated than in Test 1. We had 

fastened the five calibration standards to the Plexiglas. This produced a nonumform 

background where the reflection standards were located. An interpolation technique was 

used to approximate the Plexiglas pixel values hidden by the five reflection standards 

The same method was used to remove a small saturated area in the center of the 

Plexiglas and the horizontal fish rest. This process is illustrated in Figure 4.

The reflectance values used to convert from pixel value to reflectivity were measured in 

air and not water. We made several attempts to estimate their reflectivity in water but we 

were unable to achieve consistent results. It did appear that the ratio of reflectivities in 

air and water were constant. We assumed the use of the air reflectance values introduced 

only a scaling error.

Spatial signatures based on color moments require knowing the fish's orientation in the 

tank (i.e., facing left or right). Generally, the fish remained in the same orientation but on 

occasion reversed direction during the measurements. To speed up the analysis a single 

orientation was selected for each fish. Points in the spatial signature curves showing 

large variations could result from an incorrect orientation. This type of error was easily 

corrected but time consuming. Orientation errors of this type did not seem to affect the 

spatial signature analysis and were not corrected at this time.
ORIGINAL IMAGE USED FOR NON teOIRIyfry CORRECTIONS

Figure 4. Illustration of the interpolation technique used to standardize 

background reflectance.

The G term contains a number of factors that were assumed to be independent offish 

species. Figure 5 illustrates an important situation in which this assumption was violated. 

Figure 5 shows a digitized image of a striped bass. Also shown is a horizontal line 

profile through the fish and through the reflection standards. There are points on the fish 

line profile that exceed the value of the highest reflection standard. Conversion of pixel 

value into reflectivity produced values exceeding one. Reflectivity values cannot be 

greater than one. This is not an error in our calibration procedure, but results from a 

difference in G between the fish and the reflection standards. The reflection standards 

used in the calibration proceedure were diffuse reflectors. This means that light incident 

on the reflection standards was reflected in all directions and only a small portion of the 

light was imaged by the camera. On the other hand, the bright areas of the fish act like 

tiny mirrors. These areas were oriented so that the light from the fluorescence lights was 

reflected preferentially towards the camera, which made them appear to have a 

reflectivity greater than one. These unreasonably high reflectivities were not a serious 

problem, but do illustrate that signatures can be highly dependent on lighting geometry.

Fish Signatures

Fish signature data was obtained during two 1-week periods in June (Test 1) and 

September (Test 2) 1995. Table 1 lists the species and number of samples for species 

measured during each test. Spectral signatures often species were measured between the 

two tests. Furthermore, salmon and shad were split into categories, "small" and "large", 

for a total of 12 fish "species".
SJRIPED BASSWFH SATURATED PIXELS

Figure 5. Comparison of diffuse light reflection and preferential (mirror) 

reflection.

Morphological Signatures

Two very simple morphological signatures have been included in the signature data base: 

fish size (based on pixel count) and fish length to body depth ratio. This information was 

a by product of the spectral and spatial signatures calculations and the morphological 

signatures were included to help confirm cluster and species identification. The number 

of pixels comprising the fish mask was used to measure fish size. Pixel summation was a 

simple function for the image processing hardware and was also required for bulk 

reflectance calculations. This information was useful in the identification of large shad, 

striped bass and large chinook salmon within their respective clusters. Fish length and 

body depth calculations resulted from calculations of fish moments (see next section for a 

discussion of moments). This information provided a unique signature for white sturgeon 

and was also useful in confirming large salmon identifications.

Spectral signatures were obtained only for fish side views. Spectral images of 42 fish 

were taken during Test 1. Of these, 39 images were used to obtain spectral signatures. 

Nineteen images are taken of each fish yielding about 740 images analyzed in Test 1. 

Top and bottom view spectral images of an additional 13 fish were obtained, but were not 

analyzed or presented in this report. Side view images of 33 fish were taken during Test 

2 and 31 fish were used in the analysis. This corresponds to a total of nearly 630 images. 

An additional 18 fish were also examined but, not used in the study primarily because the 

images were not of a side view. In the two tests, nearly 1400 images were analyzed.

10
Considering the large number of images collected during this program there were very 

few lost fish images. From Test 1 images of hardhead number 2 at 620 nm and hardhead 

number 3 at the broadband green filter are missing. From Test 2 images of catfish 

number 3 at 680 nm, striped bass number 2 for both the 680 nm and the broadband blue 

filters and the chinook number 6 at 660 nm filter are missing. The missing files had 

minimal impact on the conclusions of this report. The 680 nm and 700 nm data has been 

excluded in our analysis because of inadequate lighting at these wavelengths and the 

broadband data has not been processed for this report. The software was not designed to 

check for missing data files and in these cases the reflectivity was assumed to be 0.

Analysis Procedure

Extracting the signatures from the digitized images was not technically difficult, but it 

was time consuming. To analyze the fish signature it was necessary to construct a mask 

for each fish. Since the fish moved between images, it was necessary to construct a mask 

for each color image. The mask is a computer generated image of the digitized fish 

image. Unlike the digitized fish image, the mask only has pixel values of 1 or 0. Every 

pixel in the mask corresponding to a fish pixel was assigned the value 1 while all other 

pixels are zero. Multiplication of the digitized fish image by the mask extracts the fish 

from the image and removes the unwanted background. This process is illustrated in 

Figure 6.

(6B) FISH MASK

ROTATED FISH.pNLY

Figure 6. Illustration of the masking technique applied to each image to isolate fish 

from the surrounding background area.

11
Following the image calibration procedure, fish images were multiplied by the mask to 

extract the fish and the spectral and spatial signatures. Several types of spatial signatures 

were evaluated for potential species identification. Most proved unsatisfactory but five 

signatures were selected as the most promising candidates. These five are described in 

greater detail later. This yielded a total of six types of signatures (five spatial and one 

spectral).

During Test 2 multiple images were often recorded at each filter wavelength. The images 

differed in the amount of neutral density (ND) filtering utilized. This was done to 

examine the effects of pixel saturation on the signatures. It was necessary to develop a 

decision criteria that could be used to select one image for the signature measurements. 

Three criteria were tested: the image with highest reflectivity, the image with best fit 

between pixel value and reflectivity (Figure 3D) and the image with smallest number of 

saturated pixels. Fortunately, all three criteria generally selected the same image and the 

highest reflectivity criteria was arbitrarily used as the decision criteria.

The signatures for each species were collected into a rectangular matrix with 19 columns. 

Each column corresponded to one of the 19 bandpass filters used in the study. The 

number of rows in the matrix corresponded to the number offish measured for that 

species. Separate signature matrices were obtained for the spectral signature and four of 

the five spatial signatures. The Fourier transformed spatial signature, which will be 

explained in greater detail, was treated in a slightly different manner. The fish from Test 

1 and Test 2 were also treated separately to avoid differences that could result from 

seasonal changes in fish coloration or slight differences in experimental procedures 

between the two tests. Average signatures were obtained by performing a column 

average on the signature matrices yielding the mean value for each bandpass filter. The 

standard deviation for each column was also calculated.

The spectral signature of a single fish was defined to be its average reflectivity which was 

calculated in the following manner: Multiplying the mask and the calibrated digital fish 

image extracted the fish from the background by setting all the non-fish pixels to 0. The 

fish pixels retained their calibrated value. A summation over all the pixels yielded a 

value related to the total signal reflected by the fish. A summation over all the pixels in 

the mask was performed which yielded the total number of pixels comprising the fish. 

The ratio of the total signal to the total number of pixels was the average reflectivity.

12
Table 1. Summary of spectral and spacial fish signatures of tests 1 and 2 (different 

trips).

Testl

1

2

3

4

5

6

7

8

9

10

Test 2

1

2

3

4

5

6

7

8

9

10

SPECIES

Small Chinook

Small American 

Shad

Large American 

Shad

Striped Bass

Rainbow Trout

Channel Catfish

White Catfish

Northern 

Pikeminnow

Hardhead

Sacramento 

Sucker

Small Chinook

Small American 

Shad

Striped Bass

Channel Catfish

White Catfish

White Sturgeon

Northern 

Pikeminnow

Hardhead

Sacramento 

Sucker

Chinook, Jack

DATA 

CODES

cs

shs

shl

sb

rb

cf

we

sq

hh

su

chf

sh

sb

cf

we

sr

sq

hh

su

chj

SAMPLE 

NUMBER

4

2

2

7

3

3

1

7

7

8

2

3

3

3

2

2

4

2

4

8

COMMENTS

data contains only one sample at 

this time

only six have been used

two very small hardheads not used 

in spatial signature analysis

only seven have been used

one not used because of uncertain 

identification

one NP was eliminated because it 

had an abnormally high reflective.

13
RESULTS AND DISCUSSIONS 

Spectral Signatures

The results of the spectral signature analysis indicated that fish could be grouped into 

three reflectance clusters. These clusters were labeled high, medium, and low. The high 

reflectance cluster consisted of the adult shad, shad fry, chinook salmon fry, and striped 

bass. The white catfish, channel catfish, white sturgeon, rainbow trout, and northern 

pikeminnow made up the medium reflectance cluster. The hardhead, Sacramento sucker 

and jack chinook salmon constituted the low reflectance cluster. The three spectral 

signature clusters for both tests were plotted and are shown on Figures Rl through R6 

(Appendix). For each fish species there were three curves. The central curve was the 

average reflectance value for each wavelength. The upper and lower curves were the 

average value plus or minus the standard deviation at each wavelength. The region 

bounded by the upper and lower curves provided an indication of the range of possible 

reflectance values for each species. The presentation of Figures Rl through R6 shows 

there was considerable overlap between some species reflectivities. From that graphic 

analysis we concluded that, within a cluster, reflectivity was not a definitive signature.

The overlap between the high and medium and medium and low clusters are shown in 

Figures R7 through R14. The high and medium clusters from Test 2 (Figures R2 and R4) 

have been plotted together in Figure R9. The high cluster information has been plotted in 

red and the medium cluster in blue. The solid curves are the average species reflectivity 

curves and the asterisks represent the average value plus or minus the standard deviation. 

As can be seen from Figure R9, there is very little overlap of the blue and red curves. 

The average curves are well separated and the vast majority of red asterisks lie above the 

blue asterisks. The same information was plotted in a different manner in Figure RIO. 

The high cluster was plotted as a single red bar, where each bar corresponds to a separate 

bandpass filter. The upper and lower bounds of each bar was determined by the 

maximum and minimum red asterisks from Figure R9. The medium cluster data is 

plotted as blue bars. Areas where the red and blue bars intersect are indicated by shades 

of red or blue depending on amount of overlap between the high or medium clusters. In 

most cases the bars are completely separated. Similar plots for Test 1 are shown in 

Figures R7 and R8. There was slightly more overlap, but the clusters were in general 

well defined especially at the smaller wavelengths.

The overlap between the medium and low clusters is shown in Figures Rl 1 through R14. 

The medium cluster is shown in blue and the low cluster in green. Referring to Figure 

Rl 1 or R12, the separation between Test 1 medium and low clusters is not as great as 

between the high and medium clusters but still well defined. In Test 2 (Figure R13 and 

R14) two clusters are still evident but there was considerable overlap.

14
Spectral signatures could be used to associate a fish species with a reflectance cluster. 

This was especially true for species within the high reflectance cluster. There was a 

greater overlap between the medium and low clusters, but the separate clusters were still 

evident. Association of a fish with a reflectance cluster greatly reduced the task of 

species identification through a reduction in the number of probable species. No species 

tested exhibited a distinctive spectral signature that would provide a unique identification. 

There was a general increase in reflectivity at the longer wavelengths (reds), especially in 

Test 2. It was unclear if this was evidence of a seasonal variation or a systematic error in 

our analysis procedure.

While average reflectivity provided a good estimate of the fishes reflectance cluster, it did 

not yield a unique species identification. Additional signatures were still needed to 

distinguish fish species within each cluster as well as confirm the cluster identification. 

Spatial signatures in conjunction with a few simple and basic morphological signatures 

may provide the additional information need to provide a unique species identification.

Moments

Fish with the same average reflectivity may have different spatial color distributions. 

This point is illustrated in Figure 7 which shows four similar rectangles with the same 

average reflectivity but with different spatial color distributions. Moments are a standard 

technique in shape analysis and provides a quantitative measurement of differences in 

spatial color distributions. We found with fish, it could be used to determine color 

differences from the dorsal to ventral and/or head to tail changes. The formal 

mathematical definition for the moments of an arbitrary object appear to be quite 

involved but in reality the first few moments are quite intuitive. The formal expression 

for moments my is given in Equation 3 where the summations are over all the pixels in 

the X and Y directions and p(X,Y) is the pixel value at coordinate (X,Y). The X and Y 

directions correspond to the standard Cartesian graph coordinates (horizontal and vertical 

axes).

m ij = EExj * yj * p(x,y) Equation 3

The zeroth order moment (m00) is just a summation of all the pixels comprising the 

object. In our case, if the object is the fish, then the m0;0 is just the total signal from

15
signal = 3870 

x center of mass = 11 

ycenterQfrrpss=5C 

s;xmQmeitofinerfja = 

y moment of inertia =

FIGURE 7C

signal = 3870 

x center of mass=95 

y center of mass = 50 

x moment of inertia-3201042 

y moment of inertia 838480

signal =3870

x center of mass = 100

y center of mass = 54

x moment of inertia=3289086

y moment of inertia = 777422

FIGURE 7D

signal = 3870 

x center of mass=100 

y tenter of mass = 50 

xjmoment of inertia-39B1240 

y moment of inertia = 1069080

Figure 7. Simple examples of different spatial patterns and the use of center of mass 

and moment of inertia to quantify measures of differences.

16
the fish. If the object is the fish mask then mo 0 is just the total number of pixels 

comprising the fish. The ratio of the m00 for the fish to m00 for the mask is the average 

reflectivity. The ratio of first order moments to the zeroth moment (Xc = m, 0/m0 0) and 

(Yc = m0 /moo) are the center of mass in the X and Y directions. The second moments 

(m2 o, mlfl and m0 2), which are the highest moment we will be concerned with, are more 

commonly know as the moments of inertia. The value of the moments depend on the 

choice of coordinate system. Interpretation of the moments is simplified if the center of 

mass is chosen as the origin of the coordinate system. The central moments are given by 

a simple modification to Equation 4.

Mu = ZZ(X - xc)' * (y - Yi)1 * P (x,y) Equation 4

A further simplification is possible if spatial signatures are determined in terms of the 

principal central moments. The principal central moments are calculated from Equation 4 

after the object has been rotated through an angle (q) given by

tan(2*q) = 2*Mu/( M2/M0?2) Equation 5

The rotation aligns the principal axes with the X and Y axes. This rotation was important 

because the principal axes tend to correspond to the axes of symmetry of the fish as 

perceived by human observers.

Zero through second order moments were calculated for both the fish and the fish mask. 

Moments calculated for the mask are referred by as mass moments (i.e., center of mass or 

moment of inertia). Moments calculated for the fish are referred to as color moments 

(i.e., center of color or moment of color). Color principal moments calculate from 

Equation 4 after rotation through the angle given by Equation 5 were written as CP (i.e., 

CP0 o or CP, 0). Mass principal moments were written as MP. Several spatial signatures 

were calculated from combinations of these moments. Two signatures showed potential 

as a means of distinguishing fish within a reflectance cluster. These are referred to as (1) 

Difference Between Centers of Color and Mass and (2) Normalized Moment of Color.

The Difference Between Centers of Color and Mass (DBCCM) is the X (or Y) difference 

between the center of color and the center of mass divided by the length (or body depth) 

of the fish.

DBCCMx = (CPl5o - MP1>0)/length Equation 6 

or

DBCCMy = (CP0,, - MP0;1 )/width 

The length or body depth normalization allow comparison offish of different sizes.

17
The Normalized Moment of Color (NMC) is the X (or Y) fish moment of color divided 

by the product of the total fish signal and the X (or Y) moment of inertia.

NMCx = CP2,o/(CP0,o* MP2 ,0) Equation 7 

or

NMCy = CP0y(CP0> o*MPof2 )

The moment of color was normalized in this manner to remove dependencies on fish size 

and reflectivity.

Moment and Morphological Signatures

Size

Fish size information is shown in Figures SZ1 through SZ6. The total number of pixels 

has been used is a measure offish size and corresponded to the MP0 0 moment of the 

mask. The pixel number clearly distinguishes large American shad and striped bass from 

small American shad and chinook salmon fry. Pixel size was also useful in 

distinguishing large chinook salmon from other fish in both the medium and low 

reflectivity clusters (Figure SZ6). Size varies throughout the life cycle of the fish and 

clearly fish size should be used with caution for the purposes of species identification.

Length to depth Ratio

White sturgeon have a significantly larger length to depth ratio than other species 

measured (Figures LWI through LW6). Length to width ratio also appeared to be a good 

signature for distinguishing jack chinook salmon from other fish in the low reflectance 

cluster. The small size of shad and salmon fry made it difficult to construct an accurate 

mask for these fish. Consequently, the length to depth ratio curves (Figures LWI and 

LW2) were ambiguous. However, a comparison of the shad and salmon fry in Figure 8 

shows that shad and salmon fry of similar lengths have different body depths.

Difference Between Centers of Color and Mass (DBCCM)

The Difference Between Centers of Color and Mass (DBCCM) is the X (or Y) difference 

between the center of color and the center of mass divided by the length (or width) of the 

fish.

DBCCMx = (CP1>0 - MP1;0)/length Equation 8 

or

DBCCMy = (CP0,i - MP0.i)/width

18
Figure 8. Illustration of the different body to length ratios of small 

American shad and chinook salmon fry.

This spatial signature was motivated by our need to distinguish color distribution like 

those used in Figure 7A, B and C. All three figures have the same signal (average 

reflectivity) but very different spatial distributions. Figures 7A and B can be 

distinguished by the value of the Y center of mass, while Figures 7 A and C can be 

distinguished by the X center of mass value.

Figures 7A, B and C can be compared rather easily because the rectangles are placed at 

the same location. Comparison of fish images was more due to fish movement. This 

problem was partially solved by computing the difference between the shift in center 

color and center of mass of each image. The X component of the DBCCM indicated if the 

head or tail of the fish was brighter or darker while the Y component provided equivalent 

information on the dorsal and ventral color distribution. A positive X component 

indicated that the tail was brighter than the head while a positive Y component indicated 

that the ventral area was brighter than the dorsal area. The length or depth normalization 

allowed comparison of different sized fish.

The X component (Figures DX1 through DX6) does not appear to be a useful signature. 

The curves all tend to overlap and contain both positive and negative values. The figures 

have been included for completeness. The Y component may contain some useful 

information. A comparison of Figures DY3 (Test 1) and DY4 (Test 2) showed there was 

no overlap of pikeminnow with either rainbow trout or sturgeon. White sturgeon and 

rainbow trout did overlap. This signature should be used with caution. Rainbow trout 

and white sturgeon were only available during Test 2 and there were a number of cases 

where promising signatures were not consistent between tests. An example was the 

comparison of the X Normalized Moment of Color for hardhead and sucker between 

Tests 1 and 2 (Figures DX5 and DX6).

19
Normalized Moment of Color (NMC)

The Normalized Moment of Color (NMC) is the X (or Y) fish moment of color divided 

by the product of the total fish signal and the X (or Y) moment of inertia.

NMCx = CP2 0/(CP0,o* MP2 0) Equation 9 

= CP0,2/(CP0/MP0>2)

or

The moment of color was normalized in this manner to remove dependencies of fish size 

and reflectivity. Our use of this signature was motivated by the differences between 

Figure 7A and D. The figures were clearly different but have the same center of mass 

coordinates. The two color distributions were easily distinguished by comparing 

moments of inertia instead of centers of mass. The X and Y normalized moments of 

color are shown in Figures MX1 through MX6 and MY1 through MY6. The X and Y 

normalized moments of color were similar, so only the X component will be discussed.

The NMC of striped bass and large American shad were normally lower (<1.1) than 

values for juvenile shad and salmon (Figure MX2). The standard deviation for salmon in 

Test 1 (Figure MX1) was large and did overlap with striped bass slightly. The large 

standard deviation was due to the difficulty in constructing an accurate mask for small 

fish. Note that there was almost a complete separation of the y component (Figure 

MY1). NMC for small shad and small salmon were generally greater (10~4) than for 

other fish species (7x10~5 ) studied in this project. The upper limit of the standard 

deviation of white catfish in Test 2 (MX4) did approach 10~4 . The NMC for jack salmon 

(Figure MX6) was much lower than other species in the medium and low clusters 

(<1.8xlO~5) with the exception of the lower standard deviation line of hardhead in Test 2.

Fourier Transform Spatial Signatures

The Fourier transformation is a well known technique for analyzing the frequency content 

of temporal and spatial signals. The Fourier transform expresses a complex function as a 

sum of simple sinusoids. This transformation often results in a more simple 

representation of the complex function. The following are a few examples illustrating 

how Fourier transforms were used to obtain fish signatures. Fourier transforms can have 

both real and imaginary components. It is often more useful to plot the power spectrum 

of the Fourier transform rather than its real or imaginary components. Figure 9 shows a 

pattern of 8 alternating bright and dark bars. Also shown is a horizontal line profile of the 

bar pattern. The bright bars have a value of 1 and the dark bars have a value of 0 as can 

be seen from the line profile. If this bar pattern was a fish, then the bright bars would 

have a very high reflectivity and the dark bars would have a reflectivity of zero. The 

Fourier transform power spectrum of the line profile was plotted at the bottom of 

Figure 9.

20
Several peaks in specific frequency bands were evident. The first occured at zero cycles 

and corresponded to the total signal in the pattern. If we make an analogy between the 

bar pattern and a fish, then the signal at zero cycles is related to the reflectivity of the fish. 

The second peak occurs at 8 cycles and is produced by the 8 bar pattern. The three 

smaller peaks at 24, 40 and 56 cycles are harmonics of the second peak at 8 and are due 

to the sharp edges of the bar pattern. Edge effects are illustrated in Figure 10. The bar 

pattern in Figure 9 is duplicated in Figure 10. The line profile of the bar pattern is shown 

in red. Also shown in blue is a line profile of an 8 cycle sine wave. The corresponding 

Fourier transform power spectrums are shown in red and blue at the bottom of Figure 10. 

The red power spectrum line corresponding to the line profile of the bar pattern shows the 

peaks at 8, 24, 40 and 56 cycles. The blue power spectrum line corresponding to the sine 

wave has only the peak at 8 cycles.

A second bar pattern is plotted in Figure 11 along with the corresponding line profile and 

Fourier transform power spectrum. The bar pattern again has a frequency of 8 cycles, but 

in this case the dark bars have a value of 0.5. Two differences can be seen between the 

Fourier transforms power spectra in Figures 9 and 11. The signal at zero frequency is 

greater in Figure 11. If we again make an analogy with a fish then the reflectivity of the 

fish in Figure 11 is greater than that in Figure 9 which is true because the dark bars in 

Figure 9 have zero reflectivity. Conversely the signal at 8 cycles is greater in Figure 9 

indicating a greater contrast in the bar pattern. The higher harmonics are less obvious in 

Figure 11

Figure 12 shows a third bar pattern similar to Figure 9 except that there are four bars 

instead of eight. The line profiles and corresponding Fourier transform power spectrum 

are also shown. As expected a signal peak occurs at four cycles instead of at eight. 

Higher harmonics are also visible. Finally a fourth bar pattern, line profile and Fourier 

transform power spectrum are illustrated in Figure 13. The bar pattern is created by 

adding the bar patterns in Figures 9 and 12. The evidence of bars occurring at both 4 and 

8 cycles is clearly seen in the accompanying power spectrum.

These figures hopefully illustrate the significance of Fourier transforms and ability to 

detect somewhat subtle differences. Fourier transforms provide information on a signal's 

frequency content. This includes information on the net strength of the signal and the 

strength of each frequency component of the signal

21
0

BAR PATTERN

-1

0 50 100 150 

HORIZONTAL LINE PROFLE

5000

4000

3000

2000

1000

0

0 10 20 30 40 SO' 

FOURIER TRANSFORM OF LINE PROFILE

60 70

Figure 9. Illustration of a bar pattern and how a Fourier transform power spectrum 

displays the vertical patterns to frequency bands along the Y axis.

22
BAR PATTERN

2 

1.5

1 

0.5

0 

-0.5

-1

0 50 100 150 

HORIZONTAL LINE PROFLE

5000

4000

3000

2000

1000

0

0 10 20 30 40 50

FOURIER TRANSFORM OF LINE PROFILE

60 70

Figure 10. Illustration of a bar pattern and sine wave showing how a Fourier transform 

power spectrum displays the vertical patterns to frequency bands along the 

Y axis.

23
10000

8000

60001

4000

2000

0

BAR PATTERN

0

-1

IMAMM

0 50 100 150 

HORIZONTAL LINE PROFLE

0 10 20 30 40 50 60 

FOURIER TRANSFORM OF LINE PROFILE

70

Figure 11. Illustration of a less intense bar pattern (Figure 9) and its Fourier 

interpretation in frequency band.

24
BAR PATTERN

0

-1

0 50' 100 150 

HORIZONTAL LINE PROFLE

5000

4000

3000

2000

1000

0

0 10 20 30 40 50

FOURIER TRANSFORM OF LINE PROFILE

60 70

Figure 12. Illustration of wide bands and the different way the Fourier transform 

power spectrum displays it from Figure 9.

25
BAR PATTERN

0

-1

0 50 100 150 

HORIZONTAL LINE PROFLE

5000

40001

30001

2000

1000

0

A A. .

o 10 20 30 40 50

FOURIER TRANSFORM OF LINE PROFILE

60 70

Figure 13. Illustration of a gradient bar pattern and how the Fourier transform power 

spectrum displays that on the Y frequency axis.

26
Fourier Transformation Signature Analysis Procedure

Fourier transforms were performed on each fish process for this report. Fourier 

transforms were taken along both horizontal and vertical lines. Horizontal and vertical 

are referred to as "X" and "Y" respectively. The mask principal axis angle was used to 

orient fish and Fourier transforms were obtained for horizontal and vertical lines passing 

through the center of mass. The mask principal axis angle was used instead of the fish 

principal axis angle because the resulting orientation was in better agreement with human 

perception. The zero frequency component was not included in the plots so that the 

higher frequency components could be observed. The frequency units were scaled to the 

length (or width) of the fish and are expressed as cycles per fish. Data was obtained for 

frequencies ranging between one and twenty one cycles per fish.

The resulting Fourier transform signature analysis produced significantly more data than 

other signatures. One reflectance value was obtained for each fish image but the Fourier 

transformation analysis produced twenty one frequency values per fish image. To handle 

data abundance, a single Fourier transformation curve was calculated for each species. 

The single curve was created by performing an average over all fish and over all filters 

with center wavelengths between and including 420 and 660 um. Unlike other 

signatures, this averaging procedure made our Fourier transformation signatures 

independent of wavelength.

Fourier Transformation Signatures

Fourier transformation signatures (FTS) appeared to be a promising adjunct to fish 

reflectivity as a method of species identification. The Fourier transformed power 

spectrum of a striped bass is illustrated in Figure 14. As might be expected because of 

the striped pattern, the FTS of the striped bass is similar to the FTS examples shown 

above. There were two prominent features in striped bass. A peak in the Y-axis FTS 

occured around 10 cycles per fish. This clearly corresponded to the distinguishing striped 

pattern of this species. The absence of any signal at this frequency on the X-axis made 

this a defining signature. The second distinguishing feature was a peak at 5 cycles per 

fish along the X-axis. Both peaks were clearly evident in Test 1 and Test 2 FT plots 

(Figures FT1-FT4). A comparison of striped bass and large American shad FTS (Figures 

FT5-FT6) showed two distinctive signatures. Compared to other species, both American 

shad and striped bass had significant signals out to 5 or 6 cycles. Shad had a greater 

gradual drop in both the X-axis and Y-axis signal with increasing frequency and had no 

distinctive peak at 10 cycles. Fourier transformed power spectra are shown for the shad 

and salmon fry in Figures FT7-FT14 (Appendix). We did not attempt to determine 

Fourier transform signatures for shad and salmon fry because of concerns stemming over 

possible inaccuracies caused by their small size.

27
STRIPED BASS FOURIER TRANSFORM POWER SPECTRA

0.03

0.02-

0.01 -]

0

0 5 10 15 20 

POWER SPECTRUM FOR X-AXIS (FISH LENGTH)

25

5 10 15 20 

POWER SPECTRUM FOR Y-AXIS (FISH WIDTH)

25

Figure 14. A Fourier transform power spectrum for striped bass

28
Previous hardhead and Sacramento sucker signatures were quite similar; however, their 

FTS might offer a distinctive signature. The sucker's Y-axis FTS (Figures FT31-FT34) 

appeared to dominate low frequencies in the range of one to four cycles.

Hardhead FTS (Figures FT35-FT38) was dominated by the Y-axis FTS at one cycle but 

the X-axis FTS was generally stronger at frequencies between two and four cycles. Jack 

chinook (Figure FT39 and FT40) X-axis FTS dominated frequencies above three cycles 

where there were features at both five and ten cycles. These features are distinguishable 

from striped bass because they are weaker and dominates the X-axis FTS rather than the 

Y-axis FTS. Jack chinook signatures were only measured in Test 2 and additional 

measurements are recommended to confirm the five and ten cycle features. Hard head, 

suckers and jack chinooks have much less signal throughout the frequency spectrum than 

the striped bass and large shad.

With the exception of rainbow trout, the FTS for species in the medium reflectance band 

were not distinctive and were similar to the FTS of the suckers (dominated at low 

frequencies by the Y-axis). The strength of rainbow trout FTS between 4 and 6 cycles 

was similar to that of the large shad except that the X-axis component dominates (Figure 

FT19 and 20). The strength of the low frequency FTS of pikeminnow, rainbow and 

channel catfish were greater than those by white catfish, sturgeon, sucker and hardhead. 

There can be a factor of two variations in the low frequency signal strength from Test 1 to 

Test 2, so caution should be used in deciding how much confidence to place in signatures 

based on the low frequency FTS strength. The FTS of the white catfish from Test 1 

appears to have significant features at the higher frequencies (Figure FT25 and 26). This 

is because the low frequency FTS strength is significantly lower and the strength at the 

higher frequencies is really comparable to those of the other fish.

Histograms

The distribution of the number of pixels as a function of the pixel value is know as a 

histogram. Digitized pixel values can range from 0 to 1 in increments of approximately 

0.004. Fish in the high cluster had a greater number of large pixel values compared to 

fish in the medium or low clusters groups. Histograms for each fish image were 

calculated and examples of histograms are shown in Figure 15. The X-axis has been 

converted from pixel value to reflectivity and values >1 were possible when specular 

reflections were present. Specialized computer boards are available for calculating image 

histograms. This makes histograms an attractive candidate for fish signatures. The fish 

histograms obtained in this study have not yet been reduced to effective signatures. 

Histograms showed a great deal of variability and it was not obvious how to effectively 

quantify this information. Histograms did contain several very interesting trends. For 

instance, jack chinook salmon histograms tended to have a very distinctive bifurcation 

while histograms of higher reflective fish like small chinook salmon and American shad 

were generally more symmetric (Figure 15).

29
'*siss*as^^«^^iiHMi8«^issii^iigji^ia3s^^SlMiis^;iiilll|!jpRii

Figure 15. An example of a histogram image of a fry and jack chinook salmon. (Note 

the bimodal peaks in the jack salmon that could be used as another spatial 

signature for fish identification).

Signature Database

The results of the spectral and spatial signatures are summarized in Table 2. Fish species 

are listed in the first column and grouped into their respective reflectance clusters. The 

letters H, M and L refer to the high, medium and low reflectance clusters. An L entry 

beside a fish species means that the signature in that column can be used to distinguish 

that species from all species in the low reflectance cluster.

30
Table 2. Summary offish identification based on spectral and spacial signatures.

FISH 

SPECIES

Striped Bass

Large Shad

Small Shad

Small 

Chinook

Pikeminnow

Rainbow 

Trout

Catfish

White Catfish

White 

Sturgeon

Sacramento 

Sucker

Hardhead

Chinook, Jack

SPECTRAL

Reflectance 

Band

High

High

High

High

Medium

Medium

Medium

Medium

Medium

Low

Low

Low

MORPHOMETRIC

Size

H M L*

H* M L

Length To 

Depth 

Ratio

Small 

Chinook

Small Shad

H M L

L

SPATIAL

X Moment 

Of Color

H@

H@

H* M 

L

H* M~+ 

L

~+

~%

M 

L~%

Y Center Of 

Color & Mass 

Difference

Rainbow trout 

White 

Sturgeon

Northern 

Pikeminnow

Northern 

Pikeminnow

Fourier 

Transform 

Signature

H M L

H M L

M* L

H M L

M* L

M+

M+

L

L

H M L

The asterisk beside the L in the large shad row and beside the H in the jack salmon row was used to 

indicate that size cannot be used to distinguish these species from one other.

Column three corresponds to the signatures based on size. In morphometric analysis it 

was shown that large American shad and jack chinook salmon were much larger than 

other species. The H, M and L in column 3 adjacent to large shad and jack salmon, 

indicates these species were distinguishable by size from the other studied species. Large 

American shad and jack salmon were approximately the same size but consequently size 

was not a good discriminate between these two species.

The next column presents length to width ratios. Sturgeon had a unique length to width 

ratio and the H, M and L in the entry indicated that this was a unique signature. Length 

to width ratio also looked promising for distinguishing small salmon from small shad but 

not other species. This fact was indicated enterring small salmon (small shad) in the 

small shad (small salmon) row. An L in the jack salmon row indicated that the length to 

width ratio can be used to distinguish jack salmon from other low reflectance species.

31
The normalized moment of color had a number of interesting applications. It could be 

used to distinguish the small shad and small salmon from other species with the possible 

exception of small salmon and white catfish. This was indicated by the "~+ "beside the 

M in the small salmon row and the "~+" in the white catfish row. The "~" indicates that 

there was a slight signature overlap. This signature can be used to distinguish the jack 

salmon from the medium cluster and from the low cluster with the possible exception of 

the hardhead indicated by the "~%". The "@" beside the H in the large shad and striped 

bass indicates that the normalized color moment cannot be used to distinguish these 

species but can be used to distinguish them from the small shad and small chinook. The 

"*" beside the H in the small shad and small salmon indicates a similar relation.

The difference between Y component center of mass and color was not a very promising 

signature but in comparing the graphs it did appear to distinguish northern pikeminnow 

from rainbow trout or sturgeon. Rainbow trout and white sturgeon signatures were only 

obtained during one test. More measurements are needed to confirm this signature

The Fourier transformed power spectrum (FTS) signatures appeared to be very useful. 

The FTS of the large shad, striped bass, rainbow trout and jack chinook salmon appeared 

to be unique signatures. The FTS of striped bass was particularly distinguishing. The 

FTS also appeared to be a promising means of distinguishing hardheads and suckers. The 

strength of the FTS of the northern pikeminnow and the channel catfish appeared useful 

in distinguishing these fish from white catfish, Sacramento sucker and hardhead.

The contents of Table 2 are displayed in a simplified matrix in Table 3. Fish species are 

listed in the same order in column 1 in both tables and row 1 of Table 3. Table entries 

summarize whether spectral or spatial signatures could be used to distinguish the two fish 

species. Identification is based on the graphical analysis presented in this report. Four 

symbols ("S", "Y", "N", and " -") are used to summarize the results of Table 2. An 

entry of "S" in a block of the table signifies that the corresponding fish species fall in 

different reflectance bands. An entry of" Y" indicates that one or more spatial signatures 

from Table 2 could possibly be used to distinguish the fish species. "N" means that no 

spatial signature was discovered. The symbol" " appears along the diagonal indicating 

that the row and column species were identical.

As an example, the entry for CJ (jack chinook) and SB (striped bass) (row 2-column 15) 

reads "S Y". The "S"signifies the species were found in different reflectance bands and 

the " Y" indicates that one or more spatial signature were identified that could potentially 

differentiate these species. Looking back at Table 2, these fish are in the high and low 

reflectance bands, and differ both in size and Fourier transform signatures. A review of 

Table 3 shows that the vast majority of entries contain "S Y" or "Y". We did not find 

any distinguishing spatial signatures for white catfish which would allow distinction from 

hard head or Sacramento sucker but they do fall in different reflectance bands. The 

northern pikeminnow and channel catfish are the only fish species for which no 

distinguishing spatial or spectral signatures were discovered.

32
Table 3. Summarized matrix of differentiated speciation based on spectral and or 

spacial signatures from 10 fish species from the Sacramento River, CA.

FISH

SB

LS

ss

sc

NP

RT

CF

we

STR

SUK

HH

CJ

SB

....

Y

Y

Y

SY

SY

SY

SY

SY

SY

SY

SY

LS

Y

....

Y

Y

SY

SY

SY

SY

SY

SY

SY

SY

SS

Y

Y

....

Y

SY

SY

SY

SY

SY

SY

SY

SY

SC

Y

Y

Y

....

SY

SY

SY

SN

SY

SY

SY

SY

NP

SY

SY

SY

SY

....

Y

N

Y

Y

SY

SY

SY

RT

SY

SY

SY

SY

Y

....

Y

Y

Y

SY

SY

SY

CF

SY

SY

SY

SY

N

Y

....

Y

Y

SY

SY

SY

we

SY

SY

SY

SN

Y

Y

Y

....

Y

SN

SN

SY

STR

SY

SY

SY

SY

Y

Y

Y

Y

....

SY

SY

SY

SuK

SY

SY

SY

SY

SY

SY

SY

SN

SY

.....

Y

Y

HH

SY

SY

SY

SY

SY

SY

SY

SN

SY

Y

....

Y

CJ

SY

SY

SY

SY

SY

SY

SY

SY

SY

Y

Y

....

SB=Striped Bass, LS=Large Shad, SS=Small Shad, SC=Small Chinook, NP=Northern Pikeminnow, RT=Rainbow Trout, 

CF=Channel Catfish, WC=White Catfish, STR=White Sturgeon, SUK=Sacramento Sucker, HH=Hardhead, CJ=Chinook, Jack 

Differention basis is: S=relfectance, Y=spatial, N= no spatial signature..

CONCLUSIONS

This study suggests that the development of an automated fish identification and counting 

system may be feasible. Preliminary tests have been very promising. If an automated 

fish identification and counting system is developed, it could prove instrumental in 

replacing observers at fish ladders and possibly allow monitoring in strategic locations 

within the river corridor itself.

Grouping fish species into three reflectance cluster groups appeared to be a logical first 

step in species identification. Additional signatures would help validate cluster 

assignments and help distinguish specific species within the same reflectance cluster. 

The high and medium reflectance clusters appear to have adequate separations, however, 

there was significant overlap between the medium and low clusters. The development of 

additional signatures would help increase the accuracy of speciation in these clusters. 

Size, length to width ratio, normalized X moment of color, and Fourier transformed

33
power spectrum all appear promising signature techniques for identifying jack chinook 

(and presumably larger chinook as well) from other medium and low cluster species. 

Hardheads and suckers appear to be difficult species to distinguish, however, the FTS 

appears promising. The length to width ratio appeared to be a unique signature of the 

sturgeon and also a promising discriminate between the chinook fry and shad fry. The 

normalized X moment of color was a promising method of distinguishing the chinook fry 

and shad fry from other fish in the high, medium and low clusters. The striped bass FTS 

should provided a very unique signature and also appeared promising as a means of 

identifying large shad and rainbow trout.

The signature database appeared promising but the identification of additional signatures 

is needed for several other species, such as the northern pikeminnow and channel and 

white catfish. It may be possible to distinguish the white catfish from the northern 

pikeminnow and channel catfish based on the strength of its low frequency FTS, but a 

more definitive signature is needed. Other than reflectance cluster, no distinguishing 

signature was found between the white catfish and the pikeminnow or hardhead.

RECOMMENDATIONS

A feasibility program is recommended that would expand on the fish signature data base 

and apply what has been learned to field environment. A possible test site would be at 

the Red Bluff Diversion Dam where the technology could be tested at the fish ladders and 

in the river (gates up operations).

Controlled lighting is needed to insure reliable signatures. Lighting variations across the 

camera's field of view will result in fish signatures dependent on the location of the fish 

within the scene. Intensity variations within the camera's field of view resulting from 

ripples or waves on the water surface can be similar to intensity changes due to a fish 

swimming through the field of view. Lighting configurations need to be developed that 

minimize the effects of variations in water flow. Further studies to determine the best 

lighting for optimum separation of the fish from its surroundings could be valuable to a 

FICS. VE Technologies, Inc. (Canada) has developed a special tunnel and illuminator 

for fish counting applications. It might be beneficial to purchase or construct similar 

equipment or software for experimenting with different lighting configurations.

An Imaging Technology Inc. computer vision system has been purchased to evaluate 

existing technology for an automated fish counting and identification system. Software 

has been written to isolate fish from the background image, but more work is needed 

before this software can be used in a machine vision system. This software is also needed 

to analyze the large samples required to refine and complete the signature's database.

34
ACKNOWLEDGMENTS

We wish to express our sincere appreciation to Sandy Borthwick who assisted in logistics 

and setting up holding facilities at Red Bluff. Carol Williams (USFWS), Bob Marsh 

(California Dept. Of Fish and Game) and Lloyd Hess (USBR) assisted in collecting fish 

and study logistics and to Charlie Listen and Dennis Rondorf for reviewing this report.. 

The study was funded under the US OS's Endangered and Species At-Risk Program and 

by the USBR's Mid-Pacific Region and Research and Technology Development 

Program.

LITERATURE CITED

Beeman, J. W., D. W. Rondorf, and M. E. Tilson. 1994. Assessing smoltification of 

juvenile spring chinook salmon (Oncorhynchus tshawytscha) using changes in body 

morphology. Canadian Journal of Fisheries and Aquatic Sciences 51:838-844.

Beeman, J. W., D. W. Rondorf, M. E. Tilson and D. A. Venditti. 1995. A Nonlethal 

measure of Smolt status of Juvenile Steelhead Based on Body Morphology. 

Transactions of the American Fisheries Society 124: 764-769.

Douglas, M. E., Miller R. R. and W. L. Minckley. 1998. Multivariate Discrimination of 

Colorado Plateau Gila spp.: The "Art of Seeing Well" Revisited. Transactions of the 

American Fisheries Society 127:2 pp 163-171

Hatch, D. R., J. F. Fryer, M. Schwartzberg and D. R Pederson. 1998. A computerized 

editing system for video monitoring offish passage. North American Journal of 

Fisheries Management 18:694-699.

Haner, P.V., J.C. Faler, R.M. Schrock, D.W. Rondorf, and A.G. Maule. 1995. Skin

Reflectance as a Nonlethal Measure of Smoltification for Juvenile Salmonids. North 

American Journal of Fisheries Management 15:814-822.

35
APPENDIX

Supporting Figures
1.2

TRIP1 HIGH

0.8

0.6

O

LU

LU 

CC

0.4

0.2

-0.2 

400

shs shl cs_ sb

450 500 550 600 650 

FILTER CENTER WAVELENGTH (nanometers)

700

Figure Rl. Fish categorized into the high spectral reflectance cluster from

the June 1995 tests. American shad adults (shl), American shad 

juveniles (shs), chinook salmon fry (cs) and striped bass (sb).
1.2

TRIP2 HIGH

0.8

£ 0.6

O

LLI

LLI 

01

0.4

0.2

-02 

400

sh chf sb

450 500 550 600 650 

FILTER CENTER WAVELENGTH (nanometers)

700

Figure R2. Fish categorized into the high spectral reflectance cluster from 

the September 1995 tests. American shad juveniles (sh), 

chinook salmon fry (chf) and striped bass (sb).
1.2

TRIP1 MEDIUM

0.8

u

O

LLJ

0.4

cc

0.2

Cf WC rb

400 450 500 550 600 650 

FILTER CENTER WAVELENGTH (nanometers)

700

Figure R3. Fish categorized into the medium spectral reflectance cluster 

from the June 1995 tests. Channel catfish (cf), white catfish 

(we), northern pikeminnow (sq) and rainbow trout (rb).
0.7

0.6

0.5

0.4

> 0.3

O 111

u! 0.2 

HI

or

0.1

TRIP2 MEDIUM

-0.1

-0.2

Cf we sq sr

400 450 500 550 600 650 

FILTER CENTER WAVELENGTH (nanometers)

700

Figure R4. Fish categorized into the medium spectral reflectance cluster 

from the September 1995 tests. Channel catfish (cf), white 

catfish (we), northern pikeminnow (sq) and white sturgeon (sr).
0.5

0.4

0.3

u

TRIP1 LOW

O

LU

LU

or

0.1

-0.1

-0.2 

400

su hh

450 500 550 600 650 

FILTER CENTER WAVELENGTH (nanometers)

700

Figure R5. Fish categorized into the low spectral reflectance cluster from

the June 1995 tests. Sacramento sucker (su) and hardhead (hh).
0.5

0.4

0.3

TRIP2 LOW

0.2

O

HI _i 

U. 

HI 

CC

0.1

-0.1

-0.2 

400

SU hh chj

450 500 550 600 650

FILTER CENTER WAVELENGTH (nanometers)

700

Figure R6. Fish categorized into the low spectral reflectance cluster from 

the September 1995 tests. Sacramento sucker (su), hardhead 

(hh) and chinook salmon jack (chj).
TRIP1 HIGH AND MEDIUM

400 450 500 550 600 

FILTER CENTER WAVELENGTH (nanometers)

700

Figure R7. Comparison of high and medium reflectance clustered fish from 

the June 1995 tests. Solid lines are species means and asterisks 

represent plus and minus one standard deviation. High 

reflectance cluster is in red and medium clustered fish in blue.
TRIP1 HIGH AND MEDIUM 1-

0.8-

0.6-

o

LU

U_ 

LJJ 

UL

0.4--

0.2-

HIGH

MEDIUM

LOW

0-

420 460 500 540 580 620 660 

FILTER CENTER WAVELENGTH (nanometers)

Figure R8. Comparison of high and medium reflectance clustered fish from 

the June 1995 tests. Degree of overlap is shown in blended 

color areas. Axis labels and fish are the same as in Figure R7.
TRIP2 HIGH AND MEDIUM

400 450 500 550 600 650

FILTER CENTER WAVELENGTH (nanometers)

700

Figure R9. Comparison of high and medium reflectance clustered fish from 

the September 1995 tests. Solid lines are species means and 

asterisks represent plus and minus one standard deviation. 

High reflectance cluster is in red and medium clustered fish in 

blue.
TRIP2 HIGH AND MEDIUM

1 

0.8-

 - - 

HIGH

0.6- 

O _ MEDIUM

u] 0-4~ 

en

0.2-

0-

420 460 500 540 580 620 660

FILTER CENTER WAVELENGTH (nanometers)

Figure RIO. Comparison of high and medium reflectance clustered fish

from the September 1995 tests. Degree of overlap is shown in 

blended color areas. Axis labels and fish are the same as in 

Figure R9.
TRIP1 MEDIUM AND LOW

00.5 

LU_J 

u_ 

uu 

a:

400 450 500 550 600

FILTER CENTER WAVELENGTH (nanometers)

650 700

Figure Rl 1. Comparison of medium and low reflectance clustered fish 

from the June 1995 tests. Solid lines are species means and 

asterisks represent plus and minus one standard deviation. 

Medium reflectance cluster is in blue and low clustered fish in 

green.
TRIP1 MEDIUM AND LOW 1-

0.8-

E 0.6-

o

HI _1 

u_ 

111

Of

0.4-

0.2-

0-

HIGH

MEDIUM

LOW

420 460 500 540 580 620 660 

FILTER CENTER WAVELENGTH (nanometers)

Figure R12. Comparison of medium and low reflectance clustered fish 

from the June 1995 tests. Degree of overlap is shown in 

blended color areas. Axis labels and fish are the same as in 

Figure Rll.
TRIP2 MEDIUM AND LOW

400 450 500 550 600 650

FILTER CENTER WAVELENGTH (nanometers)

700

Figure R13. Comparison of medium and low reflectance clustered fish 

from the September 1995 tests. Solid lines are species means 

and asterisks represent plus and minus one standard deviation. 

Medium reflectance cluster is in blue and low clustered fish in 

green
TRIP2 MEDIUM AND LOW 1-

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LU

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- ^^ = -1

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420 460 500 540 580 620 660

FILTER CENTER WAVELENGTH (nanometers)

JillHl'ii

Figure R14. Comparison of medium and low reflectance clustered fish from 

the September 1995 tests. Degree of overlap is shown in 

blended color areas. Axis labels and fish are the same as in 

Figure R13.
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12

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TRIP1 HIGH NUMBER OF PIXELS IN FISH -w*^shl

sb sb sb sb sfcr

shs shl cs sb

450 500 550 600 650

FILTER CENTER WAVELENGTH (nanometers)

700

Figure SI. Number of pixels per fish as a size determination signature for 

high reflectance clustered fish from the June 1995 tests.
14

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10

CO

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450 500 550 600 650

FILTER CENTER WAVELENGTH (nanometers)

700

Figure S2. Number of pixels per fish as a size determination signature for 

high reflectance clustered fish from the September 1995 tests.
x10

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cf we sq rb

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Figure S3. Number of pixels per fish as a size determination signature for 

medium reflectance clustered fish from the June 1995 tests.
x10

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400

TRIP2 MEDIUM NUMBER OF PIXELS IN FISH

cf we sq sr

450 500 550 600 650 

FILTER CENTER WAVELENGTH (nanometers)

700

Figure S4. Number of pixels per fish as a size determination signature for 

medium reflectance clustered fish from the September 1995 

tests.
X10« TRIP1 LOW NUMBER OF PIXELS IN FISH

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700

Figure S5. Number of pixels per fish as a size determination signature for 

low reflectance clustered fish from the June 1995 tests.
x10'

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su rih_ chj

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FILTER CENTER WAVELENGTH (nanometers)

700

Figure S6. Number of pixels per fish as a size determination signature for 

low reflectance clustered fish from the September 1995 tests.
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TRIP1 HIGH RATIO OF FISH LENGTH TO WIDTH

shs shl cs sb

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700

Figure LWI. Length to body depth ratio signatures of fish in the high 

reflectance cluster from June 1995 tests.
TRIP2 HIGH RATIO OF FISH LENGTH TO WIDTH

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Figure LW2. Length to body depth ratio signatures of fish in the high 

reflectance cluster from September 1995 tests.
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Figure LW3. Length to body depth ratio signatures offish in the medium 

reflectance cluster from June 1995 tests.
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reflectance cluster from June 1995 tests.
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TRIP2 LOW X CENTER OF MASS AND COLOR SEPARATION

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Figure DX6. Signatures of fish using the X component (differences in fish 

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September 1995 tests.
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reflectivity vary between the center of the fish and either the 

dorsal and/or ventral areas) in the normalized moment of 

color for fish categorized in the high reflectance cluster in 

June 1995 tests.
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Figure MY3. Signatures of fish using the Y component (differences of

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Figure MY4. Signatures offish using the Y component (differences of

reflectivity vary between the center of the fish and either the 

dorsal and/or ventral areas) in the normalized moment of 

color for fish categorized in the medium reflectance cluster in 

September 1995 tests.
14

12

LU10

u_

O 8 

CO

z

LU e

o: 4 

o

_J

o

O 2

Q 

UJ 

N 

Zi 0

onO -2

x10 TRIP1 LOW Y NORMALIZED COLOR MOMENTS OF INERTIA

SU hh

400 450 500 550 600

FILTER CENTER WAVELENGTH (nanometers)

650 700

Figure MY5. Signatures of fish using the Y component (differences of

reflectivity vary between the center of the fish and either the 

dorsal and/or ventral areas) in the normalized moment of 

color for fish categorized in the low reflectance cluster in 

June 1995 tests.
10

< 8

01

z

o

CO

Z 

LU

o: 2 

o

o 

o

Q 0

UJ

N

-2

x 10-s TRIP2 LOW Y NORMALIZED COLOR MOMENTS OF INERTIA

gj:>j-=ehp:=66jg

su hh chj

400 450 500 550 600 650

FILTER CENTER WAVELENGTH (nanometers)

700

Figure MY6. Signatures of fish using the Y component (differences of

reflectivity vary between the center of the fish and either the 

dorsal and/or ventral areas) in the normalized moment of 

color for fish categorized in the low reflectance cluster in 

September 1995 tests.
3.5

< 3

LU 

Z 

~ 2.5

O 

CO

1 2

O

2

(£ 1.5

O

O 

O

Q 1

LU

N

x 10-* TRIP1 HIGH X NORMALIZED COLOR MOMENTS OF INERTIA

a: 

o

0.5

-0.5 400

*hr~ shL-*hH- shl

shs shl cs sb

450 500 550 600 650

FILTER CENTER WAVELENGTH (nanometers)

700

Figure MXI. Signatures of fish using the X component (differences of

reflectivity vary between the center of the fish and either the 

head and/or tail areas) in the normalized moment of color for 

fish categorized in the high reflectance cluster in June 1995 

tests.
X NORMALIZED COLOR MOMENTS OF INERTIA

CD

I

K 1 5 5 cooQ3 

- (D

j M ^ <-* CD O O *^ 

W

S' s? p S, CD S. H i_i. CL C* g»

B' cr

CD

\~~/ ^ j~7 '

B - g OQ

^r1 cii ? 

X sis 3 B- § § 2 

3

1 o 

o o is (o 

n

B* g » s- a o th^ 2-1 ?iS? 

o

I

oo ; n>

r1 co

s) 73 

Og m 

o

73

I

m

1

o in

(0

v> CT

(A CT

W 

CT

N) 

tn

Xo

73

BO 

Oo

o 

m

3

m7J

j!
10

< 8

x 10-s TRIP1 MEDIUM X NORMALIZED COLOR MOMENTS OF INERTIA

or in

6 UO

COi 4

o

o: 2 

g

o 

o

Q o 

m

N

a: 

o

-2

-6 

400

we

sq sq- scf - sq- sq $3 s% sq

cf we sq rb

450 500 550 600 650 

FILTER CENTER WAVELENGTH (nanometers)

700

Figure MX3. Signatures offish using the X component (differences of

reflectivity vary between the center of the fish and either the 

head and/or tail areas) in the normalized moment of color for 

fish categorized in the medium reflectance cluster in June 

1995 tests.
14

12

P 

LU 10

U)

Z

LU fi 2 6 

O 

2 

tt 4

g 

o

O 2 

Q 

LU 

N 

Ii 0

01 O -2

2

X

, 10-5 TRIP2 MEDIUM X NORMALIZED COLOR MOMENTS OF INERTIA

-6 1 

400

cf we sq sr

450 500 550 600 650 

FILTER CENTER WAVELENGTH (nanometers)

700

Figure MX4. Signatures of fish using the X component (differences of

reflectivity vary between the center of the fish and either the 

head and/or tail areas) in the normalized moment of color for 

fish categorized in the medium reflectance cluster in 

September 1995 tests.
10

< 8

a: in

2

o

CO

1 4

s 

o 

s

o: 2 

g

o 

o Q o

in

N

x 10-s TRIP1 LOW X NORMALIZED COLOR MOMENTS OF INERTIA

a: 

o

-2

su hh

400 450 500 550 600

FILTER CENTER WAVELENGTH (nanometers)

650 700

Figure MX5. Signatures of fish using the X component (differences of

reflectivity vary between the center of the fish and either the 

head and/or tail areas) in the normalized moment of color for 

fish categorized in the low reflectance cluster in June 1995 

tests.
x10" TRIP2 LOW X NORMALIZED COLOR MOMENTS OF INERTIA

DC 

LU

O 4

LU

o: 

g

o o 

o

o LU N

_i ) I

a: 

o

X -4

-6 

400

hh hh hrf^

su hh chj

450 500 550 600 650

FILTER CENTER WAVELENGTH (nanometers)

700

Figure MX6. Signatures of fish using the X (differences of reflectivity

vary between the center of the fish and either the head and/or 

tail areas) in the normalized moment of color for fish 

categorized in the low reflectance cluster in September 1995 

tests.
10

< 8

DC 

LU

il 6 

o

CO

x ltf« TRIP1 LOW X NORMALIZED COLOR MOMENTS OF INERTIA

01 2 

O

O 

O 

Q 0

a

DC 

O

-2

-6 

400

su hh

450 500 550 600

FILTER CENTER WAVELENGTH (nanometers)

650 700

Figure MX5. Signatures of fish using the X component (differences of

reflectivity vary between the center of the fish and either the 

head and/or tail areas) in the normalized moment of color for 

fish categorized in the low reflectance cluster in June 1995 

tests.
TRIP2 LOW X NORMALIZED COLOR MOMENTS OF INERTIA

a: LU

O 4

LU

o:

o _i

O 0

o

Q 

LU 

N

OH

o z.

X -4

nfr hh htr "** hh

su hh chj

400 450 500 550 600 650

FILTER CENTER WAVELENGTH (nanometers)

700

Figure MX6. Signatures of fish using the X (differences of reflectivity

vary between the center of the fish and either the head and/or 

tail areas) in the normalized moment of color for fish 

categorized in the low reflectance cluster in September 1995 

tests.