Spectral Discrimination of a Tank Target and Clutter 

Using IBAS Filters and Principal Component Analysis 

by Karl K. Klett, Jr. 

ARL-TR-5599 July 2011 

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Army Research Laboratory 

Adelphi, MD 20783-1197 

ARL-TR-5599 July 2011 

Spectral Discrimination of a Tank Target and Clutter 

Using IBAS Filters and Principal Component Analysis 

Karl K. Klett, Jr. 

Sensors and Electron Devices Directorate, ARL 

Approved for public release; distribution unlimited. 
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Spectral Discrimination of a Tank Target and Clutter Using IBAS Filters and 

Principal Component Analysis 

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Karl K. Klett, Jr. 

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14. ABSTRACT 

Infrared images of a tank and background clutter are analyzed to investigate if the chromaticity plane can be used as a cueing 

aid for automatic target detection. Three images were acquired using the clear aperture and two classified filters that comprise 

the B-kit installed FLIR that is part of the Improved Bradley Acquisition System (IBAS). 5 by 5 pixel samples of the image are

plotted on the chromaticity plane from various locations on the target and background clutter. The multi-spectral imagery is 

transformed into a 3D color space which increases the dynamic range of the image information. Such a transformation of target 

and background pixel, to the plane formed by the second and third principal component, placed pixels at different locations in

the chromaticity plane, where their position is compared.

15. SUBJECT TERMS

Principal components, chromaticity plane, target discrimination 

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iii

Contents 

List of Figures iv

Executive Summary v

1. Introduction 1

2. Methods, Assumptions, and Procedures 1

2.1 Principal Components and the Chromaticity Plane .........................................................1

2.2 Constructing Principal Components from the Correlation Matrix ..................................2

3. Results and Discussion 4

3.1 Analysis Techniques........................................................................................................4

3.2 Background Pixel Location in the Chromaticity Plane ...................................................4

3.3 Target Pixel Location in the Chromaticity Plane ............................................................5

4. Conclusion 6

5. References 8

Distribution List 9 
iv

List of Figures 

Figure 1. Relationship between principal component transformation and the chromaticity 

plane. ..........................................................................................................................................2

Figure 2. Clear aperature IBAS image showing an M60 tank at 1 km. ..........................................3

Figure 3. Tank image extracted from figure 2. ...............................................................................3

Figure 4. Figure 3 pixel locations in the chromaticity plane (eigenvector 2 and 3). ......................4

Figure 5. Background pixel locations in the chromaticity plane. The white square box shows 

where the pixel samples were taken. ..........................................................................................5

Figure 6. Target pixel locations in the chromaticity plane. The white square box shows 

where the pixel samples were taken. ..........................................................................................6

Figure 7. Target and background separation in the chromaticity plane (see figure 1). ..................7
v

Executive Summary 

This report describes a technique to separate targets from their background, using the 2nd and 3rd

principal components which form the chromaticity plane. The chromaticity plane contains 

information about the hue and saturation (the color) of images. Three images were analyzed in 

the infrared, which were acquired from an Improved Bradley Acquisition System (IBAS) system 

between 8–14 microns. Two of the images were acquired using different IBAS filters and one 

image was obtained using no filter. 

The analysis was done on a cropped image of a tank, taken from a larger image. Small sections 

of the background and tank target were transformed into the chromaticity plane. Differences in 

pixel location in the chromaticity plane, between the tank target and the background, were noted. 

Both background and tank target hue values vary widely. The background saturation is 

approximately constant. This seems to indicate, for this analysis, a target can be differentiated 

from the background by the target or background saturation value in the chromaticity plane. 

 
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1

1. Introduction 

In the infrared, targets and their background periodically have the same contrast due to radiant 

intensity variations of objects compared to their background, the targets having large positive 

contrast at one time of the day and large negative contrast at others. In the transition from large 

positive contrast to large negative contrast, the object passes through a crossover point, which is 

the point at which the contrast difference between the object and the background is zero. 

The Improved Bradley Acquisition System (IBAS) system uses an 8–14 micron detector, and in 

the LWIR the photon flux emitted by the object is proportional to its surface emissivity. The 

radiant spectra, defined by the Planck function, is dependent only on temperature. This contrasts 

with the conditions in the VIS, NIR, and SWIR where the photon flux is primarily dependant 

upon surface reflectivity. 

This analysis investigates if principal component analysis can be used, with existing IBAS 

filters, to differentiate the emissivity of targets and background clutter. Thus, this analysis is an 

effort to distinguish infrared color, which is defined by emissivity. This multi-spectral analysis 

uses the B-Kit installed FLIR filters. Emissivities of materials vary by wavelength, and this 

analysis is an effort to take advantage of these emissivity changes at various wavelengths.

2. Methods, Assumptions, and Procedures 

2.1 Principal Components and the Chromaticity Plane 

Principal Component analysis, when applied to image processing, selects axes along brightness 

variations of the pixel data, the largest variation corresponding to the first principal component, 

the second largest variation corresponding to the second principal component, etc. These 

relationships are shown in figure 1. When applied to image processing, the first principal 

component is always aligned with the largest pixel brightness variation. The principal 

components also correspond to eigenvectors, and their construction is described in section 2.2. 

Figure 1 shows that considering pixel information is useful because the brightness information is 

separated from the color information. The largest spatial dimension of the prolate spheroid of 

transformed pixels extends along the first principal component. This first principal component 

aligns with brightness variations, and the color information is orthogonal to the first principal 

component in what is called the chromaticity plane, which is the plane formed by the 2nd and 3rd

principal components. The chromaticity plane, shown in polar coordinates, depicts hues varying 

from 0 to 360 degrees and saturation values in the radial direction. 
2

Figure 1. Relationship between principal component transformation and the chromaticity plane. 

2.2 Constructing Principal Components from the Correlation Matrix 

Three images of a scene, containing an M-60 tank at a distance of 1 km are shown in figure 2. 

These images were obtained using the clear aperature, the #1 and #2 filters of the IBAS system. 

IBAS uses a scanning linear array, which has 13 X 13 micron pixels. Each pixel, from the tank 

scene, maps 0.133 m from object space. The scene of the tank was cropped, and principal 

components calculated from this cropped image are shown in figures 4–6. 

First Principal Component 

(Brightness Direction) 

IBAS Clear Filter 

IBAS Filter 1 

IBAS Filter 2 First Principal Component 

(Brightness Direction) 

IBAS Filter 2 

IBAS Clear Filter

IBAS Filter 1

0 Degrees 

Chromaticity Plane 

(formed by plane of 2nd and 3rd 

Principal Components) 

 

Arrows show hue (angle) 

and saturation (arrow length) 

for one pixel. 
3

Figure 2. Clear aperature IBAS image showing an M60 tank at 1 km.

The elements of the correlation matrix, from which the eigenvectors (principal components) are 

constructed, are calculated is: 

(1) 

CCDi and CCDj are individual pixel values of the three images. The indices, i and j, which vary 

from 1 to 3, represent the following physical filter conventions: 1-clear aperature, 2-IBAS filter 

1, 3-IBAS filter 2. N is the number of pixels in the CCD image and n is a specific pixel in the 

CCD matrix. 

The cropped portion of figure 2, showing only the M60 tank, is shown in figure 3, along with the 

projection of the image’s pixel values in the chromaticity plane as shown in figure 4. 

Figure 3. Tank image extracted from figure 2. 

σij = [Σ୬ୀଵ

N (CCDi)n(CCDj)n) – (CCD(avg)i)(CCD(avg)j)N. 
4

Figure 4. Figure 3 pixel locations in the chromaticity plane (eigenvector 2 and 3).

3. Results and Discussion 

3.1 Analysis Techniques 

Twenty five pixels, located within a 5 X 5 pixel area, were analyzed at various locations on the 

target (the tank) and in the background. Equation 1 was used to evaluate these pixels and extract 

them from figure 3. These pixels measure the emissivity, and the transformation to the 

chromaticity plane provides a way to determine the infrared hue and saturation, represented by 

an individual pixel’s angular deviation from 0 degrees and its distance from the origin 

respectively, as shown in figure 1. 

3.2 Background Pixel Location in the Chromaticity Plane 

The following images show where pixels from different background locations lie in the 

chromaticity plane. Notice that, in this case, the background pixels map into the chromaticity 

plane along the hue orientation of zero degrees, when measured based on the convention in 

figure 1. This shows that the emissivity of the background is rather uniform, since in the 

infrared, emissivity defines color in a similar way that surface reflectivity defines color in the 

visible. Hue corresponds to color, and saturation is the depth or the intensity of the color. 
5

Figure 5. Background pixel locations in the chromaticity plane. The white square box shows where the pixel 

samples were taken. 

3.3 Target Pixel Location in the Chromaticity Plane 

The following images show where pixels from different target locations lie in the chromaticity 

plane. Note the difference between the target pixel locations (figure 6) compared with the 

background pixel locations (figure 5) in the chromaticity plane. Many pixels in figure 6 have 

hue orientations greater than 90 degrees and saturation vector lengths greater than those shown in 

figure 5. 
6

Figure 6. Target pixel locations in the chromaticity plane. The white square box shows where the pixel samples 

were taken. 

4. Conclusion 

Principal component analysis has been used to analyze an IBAS 8–14 micron image of a tank 

and the background. Pixels that make up the scene (tank and background) were plotted in the 

chromaticity plane, which is the plane formed by the 2nd and 3rd principal component vectors. 

When the pixels of the image are plotted in the chromaticity plane, saturation and hue are similar 

for various background locations. The saturation and hue, particularly the hue, show large 

variations on the target compared to the background. In this example, the background and target 

are separated (primarily in hue), as shown below, which might warrant further investigation of 

this phenomena as a target detection technique. 
7

Figure 7. Target and background separation in the chromaticity plane (see figure 1).

Background

Target 

Chromaticity Plane 

1st Eigenvector
8

5. References 

1. D'Zmura M.; Lennie P. Mechanisms of Color Constancy. J Opt. Soc. Of Am. 1986, A3, 

1662. 

2. Maloney L. T.; Wandell, B. A. Color Constancy: A Method for Recovering the Surface 

Spectral Reflectance. J. Opt. Soc. Am.1986, A3, 1673–1683. 

3. Scribner, D. Extending Color Vision Methods to Bands Beyond the Visible. Machine 

Vision and Applications 2000, 11, 301–312. 

4. Scribner, D., et al. Infrared Color Vision: Separating Objects from Background. Proc. 

SPIE 3379 (SPIE Press, Bellingham, Wash, 1998) pp. 14–17. 
9

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