spectral image based small target recognition...

ASPRS 2012 Annual Conference

Sacramento, California ♦ March 19-23, 2012

SPECTRAL IMAGE BASED SMALL TARGET RECOGNITION

Stanley Grossman, Graduate Researcher

Dept. of Geography and Geoinformation Science

George Mason University

Fairfax, VA, USA

ABSTRACT

This paper will present the important factors influencing spectral target recognition using multispectral data and

suggest a different approach to small target recognition. The methodology of directed spectral search will be

offered, including the use of spectral libraries, spectral similarity scores, and ROC curve analysis. The ability to

find specific small targets, such as Saddam Hussein’s yellow taxicab typifies this challenge. This paper will

conclude with experimental results for a proposed technique of spectral image based small target recognition. It will

also suggest the nature of further research on the topic.

KEYWORDS: directed spectral search, multispectral, spectral similarity, small target recognition, NEF

INTRODUCTION

The nature of intelligence analysis has been changing rapidly since September 11, 2001. Prior to that date, the

primary focus for intelligence agencies had been on the traditional cold war enemies and their order of battle. High

spatial resolution panchromatic imagery and finely honed visual evidence-based analysis, known as literal

exploitation, was the preferred technique (Clark 2010). However, the current and future analytic focus has moved to

the war on terror and spotlighted the war of the individual. This has necessitated a change from exploitation based

on contrast and shape to one of signature and signal. Additionally, it moves the targets of interest from large-scale

in the tens of meters to ones of small-scale, typically a few meters and less. Moreover, the move to non-literal

exploitation (methods other than visual examination) also brings the requirement to exploit other regions of the

spectrum in addition to black and white pictures. Spectral data – color – becomes a significant discriminator in

analysis.

The “Yellow Taxicab Problem” is well known to most intelligence analysts and has come to epitomize a class

of target recognition problem – with a priori knowledge of what is to be identified, discover some unknown number,

if any, of those targets in an image. The moniker “Yellow Taxicab Problem” stems from the search for Saddam

Hussein in 2003. All the while Hussein was eluding Coalition pursuit, intelligence analysts knew that he was

frequently traveling around Baghdad in a yellow taxicab; however, analysts were unable to regularly and accurately

identify such a target in satellite imagery (Moore 2004). Similar analytic problems are common, such as the attempt

to determine the openness of elections in foreign countries. For instance, virtually all national security and police

forces of a certain country used similarly shaped and colored vans. Therefore, an attempt was made to identify vans

and determine from their spatial distribution and proximity to polling places if the security forces were unduly

influencing voting. In another classic case in Somalia, identifying “technicals” – militarized pick-up trucks usually

painted white (McConnell 2009) – was difficult to accomplish using space-based imagery. This concept can be

broadened to include other analytic protocols, such as finding specific freight containers on ship decks or order of

battle searches that might include mobile missile transporter erector launchers (Bergman 1996).

There are three primary reasons for these analytic failures. First, even with an influx of more technically savvy

analysts, generally, established analysts attempt to solve detection problems with literal methods by scrutinizing

high spatial resolution panchromatic imagery – using this method even when attempting to solve what should be

color-based queries. Second, there has been a historical dearth of truly high-resolution multispectral imagery able to

discriminate targets as small as an individual vehicle. Third, most target searches have used in-scene methods

starting with a seed from the scene and matching it to other possible scenic targets or to a material in spectral

databases (traditional image classification methodology). Seed pixels are not always identifiable or even available

in the scene nor valid from scene-to-scene. Adding complexity to the challenge, the physical size of critical targets

has shrunk. National Geospatial-Intelligence Agency (NGA) analysts have stated that upwards of 80% of all target



exploitation is for targets that are “small” (within the “blur” circle of the sensor) (Stenberg, Coleman, and Avilla

2003).

Until recently, commonly available multispectral imagery has been of relatively poor quality in relation to the

needs. The introduction of commercially available Geoeye and Worldview imagery is changing this situation

providing higher spectral and spatial resolution. Solving the yellow taxicab problem will entail using different

techniques as well. Therefore, this paper will examine the use of directed spectral search – starting with a seed

library signature modeled for the scene being analyzed – and recently available higher spectral and spatial resolution

multispectral imagery to attempt improved small target recognition.

BACKGROUND

The elemental premise in exploiting the visible spectrum is that the target can be characterized through the

nature of its reflectance (Schott 1997). Therefore, libraries of target spectra can be used to classify the target if the

image spectra have been calibrated to apparent (absolute) reflectance. The act of exploiting reflectance can be

broken down into three components (Gomez 2001):

• measurement of wavelengths

• measurement of intensities

• interpretation.

The measurement of wavelengths is accomplished by the construction of a sensor array of detectors for the

prescribed bands (e.g., detectors sensitive to photons in the 0.45-0.53µm wavelengths). The measurement of

intensities is more complex. On the surface it seems to be just a matter of counting the incident photons and

converting to reflectance; however, it is actually a complex process. As shown in Figure 1, photons incident on the

sensor may arrive from a number of sources other than the target pixel (Metzler 2010). In the figure, only path F is

solar illumination (insolation) that directly strikes the target and is reflected directly to the sensor. Every other path

is extraneous data from:

• background radiance (A)

• atmospheric radiance (upwelling radiance) (B)

• target emitted radiance (C)

• insolation scattered from the atmosphere (D)

• atmospheric emission reflected off the target (downwelling radiance) (E).

In order to properly identify a signature, these extraneous sources of energy must be accounted for and then properly

compensated for.

Figure 1. Potential photon paths to the sensor (Metzler 2010).

One interesting question related to spectral target recognition that persists is (Price 1994), “How unique are

spectral signatures?” The answer in the case of Dr. Price is a resounding “somewhat”. Historically, the association

A

B

C

D

E F



of a target to a unique signature has been hampered by several factors. The first was the relatively large pixel size of

the available imagery, such as Landsat 30m pixels. Additionally, traditional problems have included the

considerable spectral variability of samples (e.g., varying leaf colors within a plant species), confusion caused by

spectral similarity of two targets, and confusion caused by composite signatures (spectral signature mixing). New

high spatial resolution imagery available from Worldview-2 should address the large pixel issue with a nominal

pixel size for multispectral images of about 2m. Manolakis et al. (2003) broke the unique signature question into

four parts:

• “Does a spectrum uniquely specify a material”?

• “Are the spectra of any material the same when observed at different times”?

• “How is the spectrum of a ground material related to the spectrum observed by the sensor”?

• “How should we compare two spectra to decide if they are the same”?

METHODS

Study Data This study will analyze imagery acquired via Worldview-2 (WV-2). Launched in 2009, Worldview-2 offers

multispectral imagery with spatial resolution as high as 1.84 meters ground sample distance (GSD) at nadir and 2.08

meters at 20° off-nadir. WV-2 also provides high radiometric resolution with an 11-bit dynamic range. In addition

to the high spatial resolution, it offers increased spectral resolution with one PAN band and eight multispectral

bands spread across the visible and near infrared spectrum (DigitalGlobe, Inc. 2011). Available multispectral bands

are listed in Table 1. The study area is located in the El Segundo, CA vicinity (approximately 33° 54’ 52” N, 118°

23’ 38” W). It is representative of urban/suburban scenes. Study area J4-4010-p009-01 contains parking lots and

surface streets surrounding an electronics plant; scene J6-2050-p001-02 is a typical mixed-use neighborhood with

suburban streets and light business with parking lots; and scene J6-2050-p002-03 contains part of the airport tarmac,

parking lots, and feeder streets. The imagery was collected in June of 2011 on two different days when the climate

is Mediterranean-like with an average day-time temperature in the mid-70’s°F along with relatively clear and dry

days; spatial resolution in the study images was between 2.2m and 2.3m GSD.

Table 1. Worldview-2 multispectral bands and wavelengths.

Band Coastal Blue Green Yellow Red Red Edge

Near-IR 1

Near-IR 2

Wavelength (nm)

400-450 450-510 510-580 585-625 630-690 705-745 117-895 860-1040

Spectral Library The Nonconventional Exploitation Factors database (NEF) has been the National Geospatial-Intelligence

Agency’s (NGA) signature/material library program of record since the 1980’s. The NEF database includes

material properties that are laboratory measured to National Institute of Standards and Technology (NIST) traceable

standards detailing optical, thermal, bulk, and electromagnetic properties (National Geospatial-Intelligence Agency

2011). In addition to the directional hemispherical reflectance (DHR) information typical of signature databases, it

also includes a material taxonomy for bulk properties, surface properties, polarimetric properties, emittance

properties, bi-directional reflectance distribution function (BDRF) measurements, and radio frequency properties.

Spectral signature fidelity runs from 0.3 to 15µm at approximately 2nm increments, which provides band effective

data for multispectral and hyperspectral sensors. The most compelling capability of the NEF is the included

algorithms and models to calculate aperture radiance, aperture effective value, and laboratory effective value

spectral signatures that model a spectrum accounting for background, emissivity, atmosphere, collection geometry,

and illumination geometry parameters.



Directed Spectral Search Approach This study will assess target recognition against a database of spectral targets via directed spectral search. In

normal classification processes, several classes would be identified from within the scene and assessed against

training data, usually from the same image, and in situ “truth” (Congalton 1991). Exploitation via directed spectral

search starts with a suspected target or an a priori target – e.g., the yellow taxicab. Therefore, it will start with a

spectrum from a spectral database or library and necessitate a search of the image for one or more instances of the

target. The “yellow taxi cab” target recognition problem only requires two classes – the target and everything else.

Furthermore, the classification will be made in reverse - seeking within the scene for pixels with spectral signatures

matching a specific, tailored to the collection and solar geometry, signature from the database.

Figure 2. The research methodology includes creation of traditionally classified in-scene data for comparison to

results from the directed spectral search using a modeled library signature.

The data processing approach is performed in three parts: image preparation, signature generation, and target

analysis (Figure 2). In image preparation, the raw image data is transformed into a set of radiometrically

comparable signatures. After a suitable image scene has been selected, the image is masked to eliminate false

detections due to similarly colored background material that is obviously out of bounds (such as buildings), leaving

a subset image chip of roads, parking lots, and related surfaces where vehicles are likely to be found. A traditional

classification (see Accuracy Assessment) is performed to generate comparison target sets. Lastly, the image subset

must be corrected for atmospheric conditions. DigitalGlobe provides the calculations necessary to translate digital

counts in Worldview-2 image data to reflectance using calibration coefficients (Updike and Comp 2010).

Atmospheric correction is accomplished through three approximations: the cosines of the illumination and viewing

angles for atmospheric transmittance, dark pixel subtraction for upwelling radiance, and modeled data from the NEF

for downwelling radiance. Adding the atmospheric compensation components to the DigitalGlobe reflectance

formula produces:

(

)

( ) (1)

where ρ is the target reflectance, L is the target radiance, Lup

is the upwelling radiance, Ldown

is the downwelling

radiance, D is the distance to the sun, τs is the transmissivity along the solar path approximated by cos(θs), τv is the

transmissivity along the sensor viewing path approximated by cos(θv), Es is the band averaged solar irradiance

normal to the target, and the final cos(θs) represents the projected area effect of the solar angle to target. This

formula provides the top-of-the-atmosphere spectral reflectance signature for each potential in-scene target pixel.

The NEF signature library has been selected for the generation of search signatures because it offers

sophisticated algorithms to model spectral signatures for varied atmospheric conditions, collection geometries, and

solar geometries. In this case, the NEF Aperture Radiance (AR) algorithm was employed to generate search

signatures based on a modeled mid-latitude marine atmosphere suitable for the study site. AR utilizes scattered



insolation, scattered background radiance, upwelling atmospheric radiance, downwelling atmospheric radiance,

blackbody radiance, atmospheric transmission, material reflectivity, material emissivity and BDRF calculations to

generate a modeled signature as seen at the top of the atmosphere by the sensor. This signature is the effective

reflectivity (Metzler 2011) and simply described is the ratio of the aperture radiance for a material and the aperture

radiance of a reference material (spectralon) under identical conditions. Using WV-2 spectral response data, the

specified collection parameters from the imagery headers, and standard model atmospheres, AR generates

appropriately modeled spectral reflectance signatures for use as the a priori seeds.

Using Figure 1as a reference, the underlying radiometric AR calculation is (National Geospatial-Intelligence

Agency 2010):

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) (2)

where LA is the apparent radiance (total radiance reaching the sensor aperture from the direction of the material), τa

is the atmospheric transmission from material to sensor, ε is the directional emissivity, LBB is the blackbody radiance

at the target temperature T, ρA is the aperture effective directional reflectivity of the material’s surface, ↓LAE is

downwelling radiance due to atmospheric emission, ρs is the aperture effective bidirectional reflectivity in the solar

direction, LS is the effective exoatmospheric solar radiance, ↑LAE is the upwelling radiance due to atmospheric

emission, and λ is the wavelength.

Three spectra were chosen to represent the problem set and comprise a red, a blue, and a yellow material (Table

2). The red and blue signatures are paint over metal from widely available automobile models while the yellow is

paint on aluminum as a surrogate for an automobile.

Table 2. Description of the NEF spectral signatures used as seeds in the directed spectral search (National

Geospatial-Intelligence Agency 2011).

Description Spectral Description

Red, weathered paint on metal - 1990 GEO

Metro automobile

Reflectance rises rapidly from distinct minima near 0.35 and 0.50 microns to a peak reflectance near 0.735 microns. This peak is followed by a strong absorption band near 0.88 microns, and rapid fall off in reflectance toward 2.5 microns. The absorption bands near 0.35, 0.50 and 0.88 microns, identify the red pigment as ferric oxide, probably hematite.

Metallic-blue, weathered paint on metal - 1986 Toyota Corolla automobile

Strong pigment absorption bands near 0.33 and 0.60 microns (with a side band near 0.69 microns) result is a residual reflectance peak centered near 0.46 microns, imparting the blue color of this paint. The shape of the spectral curve is similar to that of ultramarine blue pigment. Reflectance rises steeply from 0.69 microns to 1.3 microns, after which it is relatively flat out to 2.5 microns.

Yellow, semi-gloss, zinc chromate primer

paint on aluminum

Overall reflectance rises very steeply in the visible to 0.73 microns, then more slowly to 1.32 microns, before declining toward 2.5 microns. Absorption by an unknown pigment near 0.36, 0.60 (shoulder), and 0.83 microns account for the yellow color. For BRDF, the difference in the ss and sp bistatic scans at 0.6328 and 1.05mm indicates that the sample is primarily a volume scatterer. The sample is glossy and becomes specular as the wavelength increases.

The target analysis phase performs the directed spectral search and accuracy analysis. Even though the

classification starts at the library and reverses the classification direction, traditional spectral matching algorithms

may be employed. Any of the popular algorithms are potentially applicable. However, for this research the spectral

similarity score (SSS) algorithm was chosen. The spectral similarity score combines the results of spectral angle

mapper (SAM) with a Euclidean distance (ED) calculation to create a hybrid value (Nidamanuri and Zbell 2010).

SAM converts a known and an unknown spectrum each to a set of vectors in n-vector space where n is the number

of bands and then compares the angular distance between the two vectors. A threshold is used to determine when

two signatures are in close enough proximity to be labeled as similar with smaller angles corresponding to better

matches. When used with calibrated imagery, SAM is not influenced by topographic illumination, geometry

variations, or general albedo differences because it ignores vector magnitude and only thresholds on angular

distance (C. Hecker et al. 2008; Aspinall, Marcus, and Boardman 2002; Kutser, Miller, and Jupp 2006; The



University of Texas at Austin, Center for Space Research). The Euclidean distance is a measure of the difference in

magnitude of the two vectors varying between zero and one (Nidamanuri and Zbell 2010). Euclidean distance

potentially improves the method’s ability to find targets in shadows (Coogan 2011). The combined SAM and ED

result is sensitive to both the shape and magnitude of each spectral signature (Nidamanuri and Zbell 2011). The SSS

is built with formulas 3, 4, and 5 (C. Hecker et al. 2008; Luc et al.).

(∑

√(∑

)(∑

)

) (3)

where n is the number of spectral bands, t is the spectral reflectance of the target signature and r is the spectral

reflectance of the known signature.

√

∑ ( )

(4)

where s1 and s2 are the target and known spectra respectively and n is the number of bands.

√( ) (5)

Accuracy Assessment In typical classification processes accuracy assessment is accomplished through the presentation of some form

of confusion matrix with counts and percentages of pixels correctly and incorrectly identified. However, the

intelligence analyst’s goal is not number of pixels but number of targets and the determination of success or failure

at the fine granularity of the pixel level would most likely lead to the possibly erroneous conclusion of poor

recognition performance. The proper determination of target recognition is whether the target is positively

highlighted; stipulation by one pixel or a dozen pixels is not relevant. The very nature of the test is that of a binary

predictor and the use of receiver operating characteristic (ROC) curves may be a better measure of realization

(Gönen). ROC curves assist in assessing the accuracy of binary predictions. The key component of ROC curves is

the comparison of rates, for instance, the rate of positively predicting a result versus negatively predicting a result

(Barnes et al. 2007). These rates are presented as a related curve of sensitivity against specificity. In the case of this

study, there are three illustrative ROC sensitivities: 1) probability of detection (Pd) representing the number of true

predictions divided by the sum of true targets; 2) probability of false detections (Pfd) which is the number of false

predictions over the sum of the false predictions and true targets; and 3) probability of missed detections (Pmd) being

the number of true targets not predicted over the sum of true targets. The ROC curve is then produced by

juxtaposing a rate (sensitivity) against the SSS threshold (specificity). A point is generated for each predictive

threshold execution of the directed spectral search (e.g., target predictions with an SSS value less than or equal to

0.15). This study incremented thresholds by 0.025 from a no-detect value to an obvious over-detect value for each

scene and signature. These data pairs (e.g., [Pd, SSS] or [Pfd, SSS]) are then fit to a curve through cubic

interpolation. The intersection of the Pfd curve with the Pmd curve represents an analytic “sweet spot” where any

change in Pd will trade false detections for missed detections. This sweet spot is an arbitrary contrivance for

accuracy assessment. Actual intelligence problems would dictate threshold trade-off; for instance, the need to

absolutely find as many targets as possible may compel accepting higher false detection rates but fewer missed

targets while a need to only correctly identify a target may compel lower false prediction rates while increasing the

risk of missing valid targets. Comparison of ROC curves provides a relative method for judging acceptable

performance.

Because ground truth was not available for these images (as is typically the case for intelligence analysts), a

different performance benchmark was required. The ENVI® target detection wizard (ITT Visual Information

Solutions 2010) was engaged using the mixture tuned matched filter (MTMF) method with minimum noise fraction

(MNF) transform and standard ENVI® defaults to generate sets of traditionally classified in-scene targets.

Atmospheric compensation was performed using the ENVI® dark object subtraction method. The results of these

classifications were used in lieu of ground truth and served as performance benchmarks for comparison to the

experimental results.



RESULTS

The execution of this experiment resulted in three sets of data with each set consisting of the directed spectral

search against three signatures. This produced nine sets of ROC curves, one for each signature for each scene,

presented in Figure 3 through Figure 5. In each set, there are four curves: a red curve for performance solely against

the MTMF generated list of targets labeled “constrained” and a set of blue curves for performance against all targets

in the scene (including those not discovered by MTMF) labeled ”total”. The solid curves are the probability of

detection (Pd) against the constrained targets (red) or against all targets (blue); the dashed curve is the probability of

missed targets (Pmd); and the dot-dashed curve is the probability of falsely detected targets (Pfd). The “analytic sweet

spot” is the point where the Pmd and the Pfd cross in each set of curves. This is the point at which any change in Pd

requires trading missing detections for false detections. The sweet spot is identified with an arrow and three

statistics, 1) the spectral search score associated with the sweet spot, 2) the total Pd associated with the point, and

3) the Pfd associated with the point. The red arrow with a single statistic is the Pd performance solely against

constrained targets at the associated total (blue) sweet spot.

J4_4010_p009_01 Scene J4_4010_p009_01 included city streets, parking lots, and airport tarmac of both concrete and light

asphalt. In this scene, searching for vehicles with the modeled red signature performed well (Figure 3a). Against all

targets, the directed spectral search found 90% of the constrained targets and 86% of the total targets at the sweet

spot. The Pfd remained low at 13% for the total targets. The directed spectral search for the yellow vehicles (Figure

3b) at the sweet spot recognized 74% of all targets against a 26% Pfd; however, 95% of the constrained targets were

identified at this point. Against blue targets (Figure 3c), the algorithm again performed well with a total Pd of 82%

and 86% of the constrained targets identified.

J6-2050-p002-03 A typical El Segundo street scene is depicted in J6-2050-p002-03 (Figure 4) with suburban streets, houses, and

office and retail parking lots. Against this backdrop, red vehicles (a) were consistently identified with a total Pd of

81% and a constrained targets recognition rate of 85% against a 19% Pfd. Yellow vehicles (b) were harder to detect,

succeeding only 64% of the time but finding 75% of constrained targets. Blue vehicle detection (c) was in the

middle with a total Pd of 69% and a constrained success of 84%.

Figure 3. ROC curves for recognition rates in scene J4_4010_p009_01. The blue curves correspond to performance

rates against all targets in the scene (total performance) while the red curve represents performance against just the

MTMF generated targets.

b) a) c)



J6-2050-p002-02 Scene J6-2050-p002-02 (Figure 5) contains the immediate environs of a typical light industry (electronics) plant

with parking lots and a few city streets. Yet again, the red vehicle (a) detection performance is highest with a total

detection rate of 90% and a constrained detection rate of 94% with only 11% Pfd. Detection rates on yellow vehicles

(b) are anomalously low at only 40% total and 43% of constrained targets. Blue detection rates (c) once more are

moderately successful at 71% total Pd and 79% detection rate for constrained targets.

In terms of raw numbers (Table 3), the directed search performed significantly more robustly than the mixture

tuned match filter method, usually by a factor of two or more. It is clear that the MTMF search left a substantial

number of targets undiscovered and that the directed search covered this gap quite well. It must be noted that that

while in many cases the probability of detection is lower against the total set of targets than the constrained set, this

probability score represents a hefty increase in the actual number of targets detected. For instance, in the case of the

red targets in scene J4_4010_p009_01, the directed search discovered 90% of the MTMF generated targets but in

summation found 2¼ times the number of MTMF targets. The MTMF search only found 38% of the actual number

of targets in the scene (549).

Figure 4. ROC curves for recognition rates in scene J6-2050-p002-03. The blue curves correspond to performance rates

against all targets in the scene (total performance) while the red curve represents performance against just the


Figure 5. ROC curves for recognition rates in scene J6-2050-p002-02. The blue curves correspond to performance rates

against all targets in the scene (total performance) while the red curve represents performance against just the


b) a) c)

b) a) c)



Table 3. Comparison of the number of targets found at the sweet spot versus available targets. Base is the number of MTMF

generated targets discovered; additional is the number of targets found above the base by the directed search (SSS),

found is the sum of base and additional; and total is the number of targets generated (MTMF) and actually in the scene

(SSS). The base and found ratios are SSS:MTMF while the total ratio s MTMF:SSS.

ANALYSIS

With one exception, detection performance was similar from scene to scene for a specific signature search. For

instance, for the red signature in all scenes, directed spectral search performed relatively well, finding on average

85% of the total targets and 90% of the constrained targets at the analytic sweet spot. For the blue signatures, it

performed moderately well, finding on average 83% of the constrained targets and 74% of all targets. The yellow

signature directed spectral search performed feebly with an average of 59% against all targets but 71% of the

constrained targets at the sweet spot. However, there are anomalously low results for the yellow signature in scene

J6-2050-p002-02. This is most likely due to the statistically small number of true yellow targets (four constrained

targets and seven total targets). Removing these anomalous data yields somewhat better results of 69% total Pd and

85% constrained Pd. Directed spectral searches for red signatures performed better in all scenes while yellow (with

exclusion of the anomalous data) and blue directed spectral searches performed similarly in the moderate range.

There did appear to be a scene-based bias with searches in scene J4_4010_p009_01 outperforming the other two

scenes.

The yellow signature results are somewhat suspect, especially those for J6-2050-p002-02. In all scenes for the

red and blue vehicles, there were adequate sample sizes from near 100 to several hundred total targets. In scenes

J4_4010_p009_01 and J6-2050-p002-03, there were at least 30 yellow target vehicles allowing for reasonable

results. However, in scene J6-2050-p002-02, there were only seven total yellow targets imbuing each target

counted, missed, or falsely chosen to be overly weighted. When the detection threshold reaches a sufficient

distance, the red and yellow targets are confused so that at some point falsely detected red targets overwhelm truly

detected yellow targets. In J4_4010_p009_01 and J6-2050-p002-03, this phenomenon occurs well after the majority

of the yellow targets are detected and only a few remain. However, in scene J6-2050-p002-02, because of the low

number of valid targets, when 38% of the targets are identified (only three targets) there are already eleven false

detections, a Pfd of138%.



Figure 6. Normalized histogram of target detections versus spectral similarity scores for J4_4010_p009_01.

Normalized histograms of the final data run for each chip and signature are presented in Figure 6 through

Figure 8. The frequency of total target detection pixels (true and false) are binned by their respective spectral

similarity scores. As expected, red and yellow signature plots show a left (negative) skew with more target

pixels detected as the SSS threshold increases. While the blue signature histograms are left skewed as well,

they also present a bi-modal distribution. Examination of the detection rates in the blue signature data shows

that as the SSS threshold increases, the number of falsely detected targets eventually exceeds the number of

truly detected targets. Ultimately, this situation reverses and the second modal presentation occurs. This is

likely caused by addition of false pixels due to shadows mimicking blue targets. Eventually, the vehicle

signature similarity passes beyond the shadow signature; the curve drops back to a typical skew pattern picking

up additional true targets causing the bi-modal nature of the histogram.

Figure 7. Normalized histogram of target detections versus spectral similarity scores for J6-2050-p002-03.

The results demonstrate the influence of collection geometry on reflectance. Both J6-2050-p002-02 and J6-

2050-p002-03 are chips from the same image while J4_4010_p009_01 was collected two days earlier. Because

WV-2 is sun-synchronous, the solar geometries are comparable in both scenes. However, while the sensor elevation

is similar in the two images, the sensor azimuth is considerably different at 51.4° for J4_4010_p009_01 and 283.1°

for J6-2050-p002-02 and -03. The difference in geometry places the shadows in direct line-of-sight during imaging

for J4_4010_p009_01 but not for the other chips. This causes the more sharply defined bi-modal distribution for the

blue signature histogram and a general right-shift in all the histograms for J4_4010_p009_01.

Figure 8. Normalized histogram of target detections versus spectral similarity scores for J6-2050-p002-03.

b) a) c)

b) a) c)

b) a) c)



The statistical mean for each of the target detection histograms correlates well with the related spectral

similarity score at the analytic sweet spot; therefore, the histogram mean would be a suggested starting SSS

threshold for analysis with actual thresholds tuned up or down depending on the nature of the analysis. The

standard deviations range from 0.02 through 0.07 with eight out of nine being between 0.02 and 0.04; only the

J4_4010_p009_01 blue histogram is outside that range.

Table 4. The strong correlation between the target detection mean and sweet spot spectral similarity score

provides an analytic starting point for exploitation.

Red Yellow Blue

Mean SSS Mean SSS Mean SSS

J4_4010_p009_01 0.46 0.49 0.38 0.38 0.35 0.36

J6-2050-p002-03 0.34 0.33 0.30 0.31 0.34 0.38

J6-2050-p002-02 0.34 0.35 0.28 0.28 0.32 0.35

SUMMARY AND RECOMMENDATIONS

Spectral small target recognition was demonstrated using a directed spectral search technique. Preliminary

results showed that directed spectral search is a viable alternative for intelligence analysis and suggested a metric to

compare results (analytic sweet spot) as well as an algorithmic threshold for initial analysis. The studied method did

compensate for atmosphere, collection geometry, and illumination geometry; however, the results demonstrated

expected variability based on target-to-background contrast and collection geometry. Future work will compare

additional algorithms, the effect of better background characterization specific to an individual target, the

contribution of spectral unmixing on performance, and the influence of collection geometry on results.

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