Blood Stain Detection on Camouflage Clothing Using Machine
Learning Analysis of Noisy Hyperspectral Imagery
Nicholas V. Scott, Riverside Research, Open Innovation Center, Beavercreek, OH 45431
Sarah Jensen, Savor Safe Food, 6797 North High Street, Columbus, OH 43085
1. Introduction/Motivation:

2. Hyperspectral Imagery Data Structure:• Military, law enforcement, and intelligence agencies are often
tasked with forensic analysis-based operations in which
there is a need to quickly assess the probabilistic presenceof the wounded in remote positions.

• Averaged sample HSI spectra for clean cloth and blood smeared cloth are shown in Figure 2.
Spectral amplitudes for blood stained cloth are lower than for clean cloth. Spectral signatures for bothblood smeared cloth and clean cloth have distinctive peaks at 436 nm (blue), 547 nm (green), 612 nm(red), and 709 nm (red) related to both the true physical reflectance and the type of white lightspectrum. Blood stained cloth has small spectral minima and maxima between 420-550 nm andbetween 620-700 nm. This is due to the blood stained cloth photonic structure.

• Hyperspectral imagery (HSI), a proven technology for thedetection of anomalous substances in heterogeneousbackground environments, has seen use in the detection of
the wounded through its use as a photonic-based detection
tool. This is mainly but not exclusively in the area of imageryof blood stained clothing.
• Blood on clothing often appears in different configurations
which changes due to stresses that smear the fluid along
the material. The ability to detect this modulation is importantin assessing the versatility of hyperspectral imaging
technology in detecting surface signs on a wounded body.
• Blood detection under conditions of smearing on camouflage
cloth is explored using HSI and machine learning algorithms.
In particular, image segmentation under a variety of bloodsmear conditions is performed using non-negative matrix
factorization, competitive leaky learning, and Laplacian
eigenmap-based anomaly detection.

• Three HSI cubes were taken of blood smeared cloth targets where each possessed increasing
amounts of blood smear. RGB images of the targets are shown in Figures 3a from left to right. Each
HSI cube possessed 800 scan lines where a single line had 900 pixels. The HSI camera frame rate
was 89.8 Hz and the integration time was 0.18 seconds. The gain, the hardware spectral bin averageinterval, and scanning rotation speed was set to 0, 2 spectral bins, and 1.0 °/sec respectively.
• 201 X 201 and 61 X 61 pixel image chips were extracted (as shown by the white boxes in the RGB
images displayed in Figures 3a and the white boxes in Figures 3d respectively) and underwent threedifferent image segmentation transforms. These are shown below in Figures 3b-e.
3. Data Analysis Results for Nonnegative Matrix Factorization, Laplacian Eigenmap
Analysis, and Competitive Leaky Learning:a)• HSI characterization of blood stains on clothing is the firststep toward the creation of inexpensive multispectral imagery(MSI) technology capable of performing remote sensingbased blood stain image segmentation.
2. Hyperspectral Imagery Data Acquisition:b)a)• Nonnegative matrix factorization (NMF) factors the HSI
data into two different matrices – NMF eigenvector andabundance or weight matrices. The internal dimensional
scale of the factorization is a priori set, representing areduction in the scale of the system.
• The Laplacian Eigenmap (LE) method is a data driven,
local preservation mapping that exhumes weakcovariance structures in the HSI data. A local window isused in conjunction with an eigenmodal method applied
to a similarity graph to accentuate anomalous spatialstructures.
• Competitive leaky learning (CLL) clustering is a neuralnetwork-based algorithm where the similarity between
the input spectral data vectors and weight vectors arecalculated through numerical competition. With eachiteration, spectral data vectors become more affiliatedwith certain weight vectors allowing for clustering ofspectral signatures.
• NMF and CLL analysis were able to segment blood
stained cloth and clean cloth for the first two target
conditions shown in Figures 3b-d. This was performed
using two NMF components and four CLL clusters
respectively. There was a decrease in segmentation
power in the third target.
• LE analysis was able to do the same robustly using four
Laplacian eigenfaces for the sub-image chips delineatedin Figures 3d. Figures 3e shows how one Laplacian
eigenface segregates blood stained and clean cloth.

• A 10-inch square digital camouflage piece of cloth wasplaced inside a light diffuser box illuminated with a pseudo
sunlight spectrum light source ensuring semi-Lambertian
conditions. The experiment’s date was February 14, 2020.
• Spectral imagery measurements of the cloth with andwithout deer blood smears were made using a Resonon
Pika-L HSI camera possessing a spectral bandwidth
spanning 300-1050 nm. The camera head was 42 inchesfrom the light diffuser box and was tilted downward fromthe horizontal at approximately 60°.

4. Data Analysis Results for Nonnegative Matrix
Factorization, Laplacian Eigenmap Analysis,
Competitive Leaky Learning, and DistanceMetrics:• As the blood stain spreads in surface area whileconserving total mass, image segmentation power for all
algorithms decreases with confusion occurring between
the camouflage cloth pattern and blood stains.
c)b)• The Kullback-Leibler divergence (KLD), and the
Bhattacharyya distance (BD), distance metrics
sensitive to covariance, were calculated as a measureof the distance between clean cloth and blood stained
cloth. Large values for these quantities indicate strong
separation of clean cloth and blood stained cloth areas.
a)b)

d)

Figure 1: Experimental set up of light source,diffuser box, and camouflage cloth. a) without
blood smear and b) with blood smear. Syringe used
for blood injection is shown.

e)Figure 4: a) KLD and b) BD of principal component(PC) analyzed image data for 10, 15, and 25 PC
spectral wavelengths for different blood smeared
cloth-clean cloth targets.

• BD and KLD were small for highly smeared (large)
blood stained cloth areas and small clean cloth areas
and for the distance metrics calculated using a large
number of principal components.
5. Conclusions:Figure 2: HSI 300 band sample spectra of blood
stained and clean cloth. Legend delineates different
spectral curves for clean cloth and blood smearedcloth.

Figure 3: a) RGB images for three different blood stained cloth targets. b) First NMF component
and c) Second NMF component for three different blood stained cloth targets. d) Competitive leaky
learning four component clustering maps of three different blood stained cloth targets. 201 X 201
pixel image chips used. Physical dimensional scale is approximately 1 square foot. e) First
Laplacian eigenface for all three blood stained cloth targets. 61 X 61 pixel image chips shown.

• HSI blood stain detection using machine learning
analysis is possible, but its efficacy decreases as
blood stain smear increases in surface area.
• Small PC numbers associated with high value
distance metrics suggest that MSI technology forblood smear detection is possible.