Potassium-Phosphorus-Nitrate Detection and Spectral Segmentation Using
Polarized Hyperspectral Imagery in High Reflective Glare Environments
Nicholas V. Scott, Riverside Research, Open Innovation Center, Beavercreek, OH 45431
Jack McCarthy, University of Dayton, Dept. of Computer Science, Dayton, OH 45469
1. Introduction/Motivation

2. Hyperspectral Imagery Data Structure (cont.)b) Competitive Leaky Learning (CLL) Clustering and Class
Separation Quantification• Military and law enforcement organizations are often tasked
with target detection and scene characterization in varioustypes of environments where light scattering intensity is highrepresenting a significant source of optical noise.

• Three HSI cubes were taken of the target using the HSI camera with no polarization filter and with thepolarization filter on aligned with the camera’s vertical and horizontal center line. These configurations
allowed horizontally and vertically polarized light into the camera respectively. An RGB image of thetarget is shown in Figure 1.

• Mitigation of this noise using classical digital filters can bedifficult due to heterogenous environments which havecomplex light scattering processes. The lack of clear andtractable post-image processing methods for taking out thisphotonic noise in turn inhibits high resolution target detection
and machine learning-based pattern recognition whichfacilitate the delivery of reliable intelligence.

• Each HSI cube possessed 800 scan lines where a single line had 900 pixels. The frame rate was set
to 59 Hz and the integration time to 0.18 seconds. The gain was set to 1 and the hardware spectral
binning was set for averaging over 2 spectral bins. The scanning rotation speed was 1.0 degrees/sec.

• CLL clustering has a neural network structure with acompetitive hidden layer. A competitive neuron consistsof a vector of weights where the CLL calculates thesimilarity between the input spectral data vector and theweight vector. For every input spectral vector, thecompetitive neurons “compete” with each other to see
which one is the most similar to the input vector. With
each iteration, spectral data vectors become moreaffiliated with certain weight vectors allowing forclustering of spectral signatures.

• Polarized filters are a possible method for ambient glare
reduction by allowing only certain modes of theelectromagnetic field to be imaged providing greater scenecontrast.
• An experiment was carried out utilizing a polarized lens
attached to a Resonon, Inc. hyperspectral imagery (HSI)
camera for the purpose of exploring the degree to whichpolarized HSI scenes allow for improved (or degraded)target detection, image segmentation, and featureextraction.
• The results of this work have great implications forunderstanding not only desert areas, where reflective glare
is a potent issue potentially inhibiting defensive forces fromengaging in optimal performance, but marine environments
where surface riverine sediment-water response is strongly
tied to polarization.

• Sample HSI spectra for sand and manure from the no polarization and horizontal polarization data
sets are shown in Figure 2. Spectral amplitudes for the horizontally polarized data are slightly greater.
Spectral signatures for manure and sand are clearly different with sand exhibiting a higher spectral
amplitude due to its higher reflectivity.
3. Data Analysis and Resultsa) Principal Component Analysis (PCA) for Non-polarized, and Polarized HSI• The dark blue area in the first PC image is manure whereas the aqua/green area is sand. The red
area is part of the white tarp.
a)b)

c)

Figure 5: Four cluster
CLL image segmentationfor a) non polarized HSI, b)
horizontally polarized HSI,and c) vertically polarized
HSI. Manure segmented
into 2 groups withperimeter reflecting sand

• Using the manure and sand clustering groups, meanvalues and covariances were calculated using 100
spectral signatures from each group. The PCA bands
up to the virtual dimension number were used in thecalculation of the means and covariances.
• The Kullback-Leibler divergence (KLD), and theBhattacharyya distance (BD), distance metrics
sensitive to covariance, were calculated as a measureof the distance between sand and manure classes.
Large values for these quantities indicate strong
separation of the manure and sand.

c)Figure 3: First, second, and third PC images for a) non polarized HSI, b) horizontally polarized
HSI, c) and vertically polarized HSI. Physical dimensional scale is approximately 1 square foot.

• The virtual dimension estimate using the covariancematrix for all data sets was about 15 suggesting that thenoise floor for all 3 data sets will occur around the samecharacteristic spectral wavelength.

Figure 2: HSI 300 band sample spectra of manureand sand. Legend delineates different spectral
curves for sand and manure.

b)

mixing. a-b) Pure manure appears black and manuresand mixture dark orange. Sand appears yellow. Tarp islight orange. c) Pure manure appears black andmanure-sand mixture light orange. Sand appears darkorange. Tarp is yellow.

• Ambient lighting conditions were characterized by sunnyskies. The average air temperature was approximately
93° F during the experiment.

Figure 1: RGB no polarization image of tarp,
sand, and cow manure. Experiment took placeon July 18, 2019 at approximately 10 am.

a)• Principal components (PCs) for 100 X 100 pixel non-polarized and polarized imagery chips are shownin Figure 3. The first PC image exhibits significant contrast in all 3 data sets at comparable spectrallevels. The polarized imagery suffers noise degradation sooner than the non polarized imagery withsignificant noise being visible in the 3rd PC image.

2. Data Acquisition and Hyperspectral Imagery
Data Structure• An 8 ft. square tarp with a sand and cow manure target
was laid down on a grass lawn. The sand/manure target
was 22 ft. from the camera head which was 5 ft. above
the ground. This produced a 13-degree angle of descentwith the horizontal.

• A four-cluster heat map is shown in Figure 5a-c for nonpolarized and polarized HSI. The figures demonstrate
clear distinction between grass, sand, manure, and tarp.

Figure 4: Eigenvalues for nonpolarized, horizontally polarized, andvertically polarized imagery. Legend
designates specific HSI data set.

• Eigenvalue spectra for all 3 data sets are shown in
Figure 4. All 3 data sets demonstrate that over 80% of
the variance is captured in the first PC. The vertical andhorizontally polarized spectra have a higher spectral
amplitude and seemingly attain a ‘spectral elbow’ slightly
earlier than the non polarized spectrum. This suggeststhat the polarized imagery is efficiently pushing moreenergy into fewer spectral bands.

Figure 6: a) KLD and b) BD of PCA image data for
10, 15, and 20 PCA spectral wavelengths for the 3
HSI data sets.

• BD and KLD for sand and manure classes for thehorizontally polarized HSI are slightly higher than for
the non polarized HSI indicating polarized imagery
class separation power that is on par with nonpolarized imagery. The BD suffered negative
infinities around PCA spectral wavelength cut off of 22
suggesting a class spanning space less than 22.
• Results suggest that HSI target detection power ispossibly slightly augmented via the use of ahorizontal polarization filter.