active spectral imagingactive spectral imaging melissa l. nischan, rose m. joseph, justin c. libby,...

• NISCHAN, JOSEPH, LIBBY, AND KEREKESActive Spectral Imaging

VOLUME 14, NUMBER 1, 2003 LINCOLN LABORATORY JOURNAL 131

M imagingsystems, operated from ground-, airborne-,and space-based platforms, have found a

variety of civilian and military applications. Thistechnology has applications in fields that range fromenvironmental monitoring and geology to mapping,military surveillance, and reconnaissance. The powerof multispectral and hyperspectral imaging arisesfrom the ability to image a scene rapidly in tens tohundreds of spectral bands. The spectral signaturesacquired in this fashion make it possible to discrimi-nate among different types of materials.

Operating at visible through shortwave infraredwavelengths (0.4 µm to 2.5 µm), passive multispectraland hyperspectral imaging systems measure reflectedsolar radiation. The radiance measured at the sensorrepresents a combination of the solar spectrum, theatmospheric absorption along the path from the sunto the scene to the sensor, the inherent reflectanceproperties of objects in the scene, the illumination ge-

Active Spectral ImagingMelissa L. Nischan, Rose M. Joseph, Justin C. Libby, and John P. Kerekes

■ With the ability to image a scene in tens to hundreds of spectral bands,multispectral and hyperspectral imaging sensors have become powerful tools forremote sensing. However, spectral imaging systems that operate at visiblethrough near-infrared wavelengths typically rely on solar illumination. Thisreliance gives rise to a number of limitations, particularly with regard to militaryapplications. Actively illuminating the scene of interest offers a way to addressthese limitations while providing additional advantages. We have been exploringthe benefits of using active illumination with spectral imaging systems for avariety of applications. Our laboratory setup includes multispectral andhyperspectral sensors that are used in conjunction with several laser illuminationsources, including a broadband white-light laser. We have applied active spectralimaging to the detection of various types of military targets, such as inert landmines and camouflage paints and fabrics, using a combination of spectralreflectance, fluorescence, and polarization measurements. The sensor systemshave been operated under a variety of conditions, both in the laboratory andoutdoors, during the day and at night. Laboratory and outdoor tests have shownthat using an active illumination source can improve target-detectionperformance while reducing false-alarm rates for both multispectral andhyperspectral imagers.

ometry, and the sensor system characteristics. Formany applications, this dependence on the condi-tions under which measurements are made can com-plicate analysis, making it difficult, if not impossible,to compare spectral data taken under different condi-tions. In these cases, it becomes important to convertthe data from radiance units (measured at the sensor)to reflectance units (an inherent property of a mate-rial). The reflectance spectrum of an object is deter-mined by its physical composition, and provides afingerprint that can be used for material classificationand identification.

The utility of reflectance spectra is ultimately de-termined by the accuracy of the data conversion fromradiance to reflectance units and the conditions underwhich measurements are made. For example, changesin the angles between the sun, target, and imagingsystem can affect the reflectance, and also introduceshadowing within a scene. In shadowed regions, theillumination may differ in both amplitude and spec-


132 LINCOLN LABORATORY JOURNAL VOLUME 14, NUMBER 1, 2003

tral shape from areas that are directly lit. Variations inspectral illumination in a scene increase the uncer-tainty in the derived reflectance spectra, making ma-terial identification more difficult and false alarmsmore common.

An active spectral imaging system has the potentialto alleviate the aforementioned problems as well asoffer additional benefits. Placing the illuminationsource on the sensor platform provides a constantangle between the source, target, and sensor. This ge-ometry eliminates illumination-angle variations thatcan complicate analysis and degrade performance.Laboratory and outdoor tests have shown that usingan active source also drastically reduces shadowing,which reduces false-alarm rates. Moreover, active illu-mination enables a sensor to operate day or night,even under adverse weather conditions when solar il-lumination is greatly reduced. If the illuminationsource is pulsed, photons reflected from an interven-ing obscurant between the sensor and the target (e.g.,a tree canopy or camouflage netting) can be removedthrough time gating. Discrimination capabilities canbe further enhanced through the addition of polariza-tion and fluorescence measurements. Using an activesource with a known output polarization simplifiesmeasurement of polarization signatures, which can beused to distinguish man-made from natural materials.And when the appropriate stimulation wavelengthsare used, laser illumination can be used to inducechlorophyll fluorescence, a vegetation marker.

Laser illumination in a sensor system is not a newconcept. A coherent laser radar that uses a carbon di-oxide (CO2) laser was developed in 1967 [1]. Otherearly proposed applications of laser illumination in-clude improving signal-to-noise ratio, mitigatingbackscatter from intervening atmosphere, and ob-taining relative target reflectance [2]. More recently,semiconductor and microchip lasers have played akey role in the development of three-dimensional im-aging laser radar (ladar) systems [3]. The active spec-tral imaging program at Lincoln Laboratory has beenexploring the benefits of combining laser and laser-like illumination with compact hyperspectral andmultispectral imagers. The program initially focusedon phenomenology associated with hyperspectral de-tection of targets and backgrounds when using active

illumination [4]. We studied targets with relevance tomilitary applications, such as inert land mines andvarious camouflage materials and paints. Subse-quently, we expanded our investigations to includeactive multispectral imaging and conducted a series oflaboratory and outdoor tests to demonstrate detec-tion of concealed targets in natural backgrounds. Theremainder of this article describes our equipment,laboratory setup, analysis techniques, and results oflaboratory and outdoor measurements.

Equipment and Laboratory Setup

We assembled a pair of sensor systems for hyperspec-tral and multispectral imaging in the visible and near-infrared (VNIR), ~0.4 µm to 1 µm, and shortwave in-frared (SWIR), ~1 µm to 2.5 µm, wavelength regions.The VNIR and SWIR hyperspectral systems were de-veloped for laboratory-based phenomenological stud-ies, providing images with high spatial and spectralresolution. Both hyperspectral systems can be con-verted to multispectral by simply changing the meansof spectral dispersion. The multispectral imagers,with their compact size and a coarser spectral resolu-tion that increases the data-collection rate, are moresuited for outdoor measurements than are the hyper-spectral imagers.

Each of the systems can be used with or without anactive-illumination source. The choice of illumina-tion source depends on the application. For phenom-enological investigation that requires high-resolutionspectral measurements, a white-light laser is used forillumination. This compact laser source, developed atLincoln Laboratory [5], generates broadband spectralillumination (532 nm to ~850 nm) while retainingthe high-brightness, subnanosecond-length pulsecharacteristics of its pump laser. (Additional informa-tion on this laser can be found in the sidebar entitled“White-Light Laser.”) For fluorescence and polariza-tion imaging, the higher-power single-frequency (532nm) output of the pump laser is used directly. Resultsfrom phenomenological measurements made in theSWIR indicate an interesting anomaly-detectiontechnique that requires using just two spectral bands[6]. On the basis of these results, we obtained 1410-nm and 1600-nm continuous wave (CW) lasers, eachwith 500-mW output power.



Figure 1 illustrates how the individual componentsof each system can be combined to operate as eitherhyperspectral or multispectral imagers. The VNIRsystem uses a silicon charge-coupled device (CCD)focal-plane array, with either a grating-based slit spec-trometer for hyperspectral measurements, or indi-vidual bandpass filters for multispectral measure-ments. (The center wavelength of the filters rangesfrom 400 nm to 950 nm in increments of 50 nm with~40-nm spectral width.) The SWIR system uses anindium gallium arsenide (InGaAs) focal-plane array,with either an acousto-optical tunable filter (AOTF)for hyperspectral measurements, or individual band-pass filters for multispectral measurements. The

AOTF has 10-nm spectral resolution with continu-ous coverage from 1.2 µm to 2 µm. Although theAOTF can be tuned over this entire range, the longestwavelength detectable by the InGaAs camera is 1.7µm. The SWIR filters range from 1 µm to 1.9 µmwith widths of 20 nm to 100 nm.

The output of a hyperspectral or multispectral im-ager can be thought of as a three-dimensional imagecube with two spatial dimensions and one spectral di-mension. Figure 2 shows an example of a hyperspec-tral image cube, in which each pixel in the two-di-mensional array has an associated spectral signaturethat represents the spectral contributions from everyobject in the pixel. Because only two of the dimen-

FIGURE 1. Four possible spectral imaging system configurations: visible and near-infrared (VNIR) hyperspectral, VNIRmultispectral, shortwave infrared (SWIR) hyperspectral, and SWIR multispectral. The hyperspectral systems were devel-oped for laboratory-based phenomenological studies to provide images with high spatial and spectral resolution. Both hy-perspectral systems can be converted to multispectral by simply changing the means of spectral dispersion. With theVNIR system, the spectrometer is replaced with a set of VNIR bandpass filters; with the SWIR system, the acousto-optictunable filter (AOTF) is replaced by a set of SWIR bandpass filters. More than the hyperspectral systems, the multispectralsystems are suitable for outdoor measurements because of their compact size and a coarser spectral resolution that in-creases the data-collection rate. Separate detectors must be used for the VNIR and SWIR systems—a silicon charge-coupled device (CCD) focal-plane array and an indium gallium arsenide (InGaAs) focal-plane array, respectively.

VNIR

Spectrometer

Hyperspectral

Bandpass filters

Multispectral

CCD detector

Illuminator

SWIR

InGaAs detector

Illuminator

AOTF

Hyperspectral

Bandpass filters

Multispectral



- , devel-oped at Lincoln Laboratory, isbased on a 532-nm diode-pumped microlaser. The 532-nmmicrolaser, shown in Figure A, ispumped by a 3-W near-infrared(808 nm) diode laser coupled tothe microlaser by 1 m of opticalfiber. The microlaser is passivelyQ-switched to produce shortpulses (~400 psec) at high repeti-tion rates, up to 16 kHz, with ap-proximately 5 to 8 µJ of energyper pulse. The output from the532-nm diode-pumped micro-laser is focused into a single-

mode silica fiber (core diameter~6 µm) of approximately 100 min length to generate the broad-band output. Intensities greaterthan 10 GW/cm2 are achieved inthe fiber core, and within a fewmeters of propagation a Stokes-shifted stimulated Raman scat-tering (SRS) pulse is generated.This pulse, which occurs at 440cm–1 from the 532-nm radiation,is spectrally broadened because ofthe inherently large Raman gainbandwidth (~40 THz) of silica fi-bers. This initial Stokes-shiftedpulse induces a second-order SRS

pulse, which is further spectrallybroadened, that pumps thehigher-order Stokes shifts. Aftereight to ten Raman shifts, thepulses are sufficiently broadenedthat they overlap to form a con-tinuum. For fibers of ~100 m,the continuum extends fromabout 680 nm to 900 nm, asshown in Figure B. The energyconversion from the green 532-nm input to the broadband“white light” output is between10 to 15%. The high repetitionrate and short pulse structure ofthe input beam are preserved.

W H I T E - L I G H T L A S E R

sions can be acquired at once, there are two differentmodes of operation possible. In the staring mode, theentire scene is imaged onto the focal plane, one spec-tral band at a time. In the scanning mode, the spectraldimension and one spatial dimension are imaged

onto the focal plane, and the entire system is scannedspatially across the scene.

Only the VNIR hyperspectral system operates in ascanning mode, imaging a single vertical strip, onepixel wide, through a grating-based slit spectrometer,

Inte

nsity

0.5 0.6 0.7 0.8 0.9 1.0Wavelength ( m)µ

FIGURE A. Simplest embodiment of passively Q-switched microchip laser. The microchip laser is at-tached to the end of an optical fiber, through which it ispumped with an 808-nm diode laser.

FIGURE B. White-light laser output spectrum. Indi-vidual Raman peaks start at 532 nm and broaden into acontinuum by approximately 680 nm.



FIGURE 2. Example of a three-dimensional hyperspectral image cube. Two dimensions are spatial, and onespectral. Each pixel contains a spectrum that represents a combination of the spectral contributions fromevery object in the pixel.

FIGURE 3. Operation of the VNIR hyperspectral imager. The system acquires a spectral signature for everypixel in the vertical slit field of view of the imager. The system is scanned in azimuth to build a three-dimen-sional hyperspectral image cube. The laser divergence is matched to the field of view of the imager.

as shown in Figure 3. The spectrometer disperses thelight onto the CCD, such that one dimension repre-sents spatial sampling and the second dimension rep-resents spectral sampling. The whole setup ismounted on a tripod and scanned in azimuth acrossthe scene to acquire the second spatial dimension,completing the image cube. The vertical entrance slitof the spectrometer determines the instantaneousfield of view of the imager. Cylindrical optics are usedto shape the outgoing laser beam to match the field ofview of the imager.

Data Processing

Before the raw output of the sensor can be used, thedata must be corrected for sensor-related effects. This

initial preprocessing includes wavelength calibrationand correction for detector nonuniformity, sensor ar-tifacts, and background levels. After the data havebeen corrected, further analysis steps are determinedby how the data will ultimately be used. For example,detecting and identifying specific materials or targetsby using a reference spectrum requires high-resolu-tion spectra plus conversion of the data to reflectanceunits to remove environmental and sensor effects. Re-moving these effects allows us to compare spectrataken under different conditions. In contrast, detect-ing anomalies or doing broad classification of the pix-els in a scene can often be accomplished with lower-resolution spectra, and the data may not need to beconverted to reflectance units.

yx

y

White-light laser

Slit spectrometer

λ

Wavelength ( m)

Rel

ativ

e br

ight

ness

µ

Pixel



In our experiments, the goal was usually targetidentification or anomaly detection. Figure 4 outlinesthe processing steps performed in each case. For tar-get identification, the spectrum from each pixel in thedata set is compared to a library of reflectance spectrafor various target and background materials in an at-tempt to find the best match. Most of the libraryspectra we use were compiled from high-resolutionscans of the targets taken in the laboratory, and somespectra were drawn from the Aster Spectral Library.*

Before a meaningful comparison between libraryand image spectra can be made, the image cubes areconverted from radiance units to reflectance units byusing one of many existing techniques. An extensivediscussion of these methods is beyond the scope ofthis article, but numerous references can be found

[7]. We used an in-scene calibration panel withknown spectral characteristics as a reference to nor-malize the data. The calibration panel has a reflec-tance of almost 99% over the VNIR-SWIR spectralrange and is a well-characterized, highly Lambertianscatterer. This technique has the advantages of beingboth accurate and easy to implement, and the disad-vantage of being impractical to use in a noncoopera-tive scenario.

Once the data have been converted to reflectanceunits, a variety of spectral detection algorithms can beused to find and identify targets in the scene [8]. Twoof the algorithms that we use for processing are spec-tral angle map (SAM) and Euclidean minimumdistance (EMD). These simple distance measuresquantify the difference between a library referencespectrum and a measured spectrum by treating themas a vector in spectral space and calculating the cosineof the angle between them (in the case of SAM) orcalculating the geometric distance between them (inthe case of EMD). These detection methods are far

FIGURE 4. Hyperspectral/multispectral data processing procedure. Raw sensor data must first undergo a prepro-cessing step that includes calibration and correction. In our work, the next step is processing related to either tar-get identification or anomaly detection. The target-identification processing begins with converting the data to re-flectance units. Then we apply spectral distance or matched-filtering algorithms to identify targets. For anomalydetection, conversion to reflectance units is not a prerequisite. Spectral band ratios and principal-componentanalysis are two examples of anomaly-detection techniques that can be applied to the data.

* The ASTER Spectral Library includes data from three other spec-tral libraries: the Johns Hopkins University Spectral Library, the JetPropulsion Laboratory Spectral Library, and the United StatesGeological Survey Spectral Library. More information can be foundat <http://speclib.jpl.nasa.gov>.

Preprocessing • Background subtraction • CCD nonuniformity correction • Wavelength calibration

On-chip or software spatial/spectral binning

Processing • Band ratios• Principal components

Anomaly detection

Processing • Spectral distance • Matched filtering

Target identification

Conversion to reflectance



from optimal, but they are computationally efficientand they have reasonably good performance for ourapplications.

We use two approaches to anomaly detection. Oneapproach involves detecting small, localized spectralanomalies, such as man-made objects, in an otherwisenatural background. We search for a small number ofpixels whose spectra differ from the local or the globalbackground spectra. The second approach uses a dis-tinguishing spectral feature in the target that enablesdetection with just a few spectral bands.

As a final step, the results of either target identifica-tion or anomaly detection can be combined withpolarization and/or fluorescence measurements to en-hance target-detection performance and reduce false-alarm rates.

Fusion of Hyperspectral, Polarization,and Fluorescence Data

In this section we demonstrate the benefit of combin-ing polarization and fluorescence data with active hy-perspectral data. These three types of measurementsprovide complementary information that can becombined to enhance detection performance andreduce false alarms.

A number of studies have demonstrated the use ofpolarimetry to detect man-made objects in a naturalclutter background [9]. Polarimetry offers an addi-tional discriminant because naturally occurring ob-jects such as grass, soil, and other rough surfaces tendto depolarize incident light more than smootherman-made objects. Consequently, a scene imaged atorthogonal polarizations will show variations in theintensity of the detected light that depend on the rela-tive smoothness of the objects in the scene.

There can be situations in which a natural back-ground, such as a smooth, broad leaf plant, shows asmuch polarization as some man-made materials. Inthis case, laser-induced fluorescence imaging, usedprimarily to monitor vegetation for signs of stress anddisease [10], allows us to discriminate between veg-etative and non-vegetative materials. With this tech-nique, a laser source excites chlorophyll fluorescence,which has spectral peaks at 685 nm and 740 nm.

To test the fusion of active hyperspectral, polariza-tion, and fluorescence data, we constructed a target

scene in the laboratory. Figure 5 shows a photo of thetarget scene, which contains an empty metal shell cas-ing and three different types of plastic (similar to theplastics used in antipersonnel and antitank landmines). The scene was imaged by using the VNIR hy-perspectral imager with the white-light laser for illu-mination. For the polarization measurement, the ver-tically polarized 532-nm pump laser illuminated thescene. A polarizer was placed in front of the detector,and the scene was imaged with the receive polariza-tion both parallel and perpendicular to the outgoingbeam. Then the polarizer was removed from the de-tector and a fluorescence measurement was taken,again by using the 532-nm laser for illumination.

Initial preprocessing of the hyperspectral data cubewas performed as described earlier. We applied theSAM and EMD algorithms to detect the targets inthe scene. A library of target spectra, including vari-ous plastics, metals, paints, and vegetation, had beencollected earlier in separate measurements. These ref-erence spectra served as inputs into the SAM andEMD algorithms, which worked effectively at detect-ing the plastics, but were not able to detect the metalshell casing, as shown in Figure 6. To detect the shellcasing, we used the additional discrimination pro-vided by the polarization and fluorescence data.

The two orthogonal polarization images were usedto calculate the degree of polarization (DOP), whichis a measure of the amount of polarization in the laserlight reflected from objects in the scene. The DOP is

FIGURE 5. Digital-camera photo of the target scene, con-taining soil, vegetation, rocks, sticks, three different types ofplastic, and a metal shell casing.

Plastic CPlastic C

MetalMetal

Plastic APlastic A

Plastic BPlastic B



the difference of the two orthogonal polarizationmeasurements divided by their sum:

DOP ||

||

=−

+⊥

⊥

I I

I I,

where I|| is the parallel polarization component and I⊥is the perpendicular polarization component. Objectswith smooth surfaces tend to have a higher DOP thanobjects with rough surfaces.

Figure 7 shows grayscale images of the target sceneshowing DOP and fluorescence imaging. HigherDOP values appear brighter and lower DOP valuesappear darker. Some of the smooth, flat leaves in thescene have a DOP as high as the metal targets, which

indicates that polarization alone is unable to distin-guish between the two materials. We used fluores-cence imaging to highlight the vegetation in the sceneand eliminate the false detections in the DOP detec-tion of the metal shell casing. Figure 8 shows the de-tections, including the metal shell casing, that resultfrom the fusion of the active hyperspectral, polariza-tion, and fluorescence data.

Shadow Reduction

We live in a three-dimensional world, and three-di-mensional objects cast shadows. Within the shadows,what little illumination exists is a combination of dif-fuse sky illumination and secondary scattering fromnearby objects. Because the exact composition of the

FIGURE 6. The hyperspectral image cube (upper right) processed by using library spectra with spec-tral angle map (SAM) and Euclidean minimum distance (EMD) algorithms, resulting in detection ofthe plastic, but not the metal, targets. Detections are indicated in red. Note that the color balance inthe rendition of the hyperspectral image cube is skewed by the lack of blue wavelengths because theshortest wavelength in the white-light laser illumination is 532 nm (green).

Spectral distance measurewith SAM and EMD algorithms

Hyperspectral detections

0.3

0.2

0.1

0.3

0.2

0.1

0.3

0.2

0.1

0.50 0.55 0.60 0.65 0.70 0.75 0.80

Ref

lect

ance

Wavelength ( m)

Library spectra

Plastic A

µ

Hyperspectral image cube

Plastic B

Plastic C



shadow illumination is unknown, the retrieved reflec-tance spectra from shadow regions are unreliable.Shadow regions are a problem for passive sensors be-cause the illumination and viewing angles are almostalways different, which results in shadows in thescene. By using an illumination source at the sensorplatform, we can force the illumination and viewinggeometries to be the same. This arrangement greatlyreduces shadows, which increases the visibility of tar-gets and decreases the false-alarm rate [11].

To demonstrate the effects of shadow reduction,we acquired a hyperspectral image cube of the scenepictured in Figure 5, first using ambient illumination(fluorescent room lights) and then using active illu-mination with the white-light laser.* Figure 9 showsgrayscale single-band (572 nm) images taken underambient and active illumination. Although theshadow regions in the ambient image have threetimes the amount of light as the same regions in theactive image, the circular shapes of plastics A and Bare more discernible in the active image. This effect,seen across all spectral bands, occurs because the ac-tive illumination source is aligned with the detector,allowing more direct light to reach the targets, whichin turn reduces shadows and increases visibility.

To quantify the benefits of shadow reduction, weused the SAM algorithm to find and identify the twoplastic mine-like objects (plastics A and B in Figure 5)in both the ambient and active data sets. Spectralangles between each pixel in the two data sets and the

library spectra were calculated. The resulting spectralangle maps were then compared to a truth map thatindicated which pixels in the scene consisted entirelyof plastic A or plastic B. A spectral angle thresholdwas set for each target such that 60% of the targetpixels were correctly identified. With these thresh-olds, we calculated false-alarm rates on a per-pixel ba-sis for each data set and target. This procedure wasthen repeated on both data sets for varying levels ofspectral resolution. Lower spectral resolution wassimulated by binning adjacent bands to achieve thedesired spectral bandwidth.

Figure 10 compares the false-alarm rates for the ac-tive and ambient data sets for varying spectral resolu-tions. For both targets, the false-alarm rates for the ac-tive image cube are significantly lower than those ofthe ambient image cube by as much as two orders of

FIGURE 8. Detections, in red, of the plastic and metal tar-gets. Fusion of the spectral-reflectance, polarization, andfluorescence data allows us to detect the metal shell casing(bottom left), which we could not detect with active hyper-spectral data alone.

FIGURE 7. Degree of polarization of the target scene, left,and a corresponding fluorescence map, right. Note thatsome of the smooth, flat vegetation returns high polariza-tion values, which could be interpreted as possible targets.Fluorescence imaging, which identifies vegetation, helpseliminate some of these possible targets.

FIGURE 9. Ambient (left) and active (right) images taken at572 nm. Notice the increased contrast in the actively illumi-nated image

* We refer to data taken under ambient illumination as the ambientdata, and to data taken under active illumination as the active data.

Plastic A Plastic B



magnitude. This improvement is entirely due toshadow reduction. The lower false-alarm rate for ac-tive illumination holds even as the spectral resolutionof the data is degraded. It appears that for plastic B,the false-alarm difference between the active and am-bient images gets smaller as spectral resolution de-grades. This effect is an artifact of the white-light laserspectrum. Spectral binning of data taken with thewhite-light laser involves binning over successiveRaman peaks (see Figure B in the sidebar entitled“White-Light Laser”). The phase of the spectral binwith respect to the Raman peaks changes the signal-to-noise ratio of the binned data, causing the appar-ent false-alarm convergence for ambient and activedata as spectral resolution decreases. Even under thesenon-ideal conditions, false-alarm rates for the activedata are significantly lower than those of the ambientdata.

We note that the false-alarm rates quoted here arequite high. Because the image contains about 105 pix-els, a false-alarm rate of 10–4 indicates about 10 falsealarms in this scene. However, no spatial processinghas been performed to reduce the false-alarm rate,

and requiring 60% of the visible target pixels to fallwithin our “detection” threshold is a relatively strictrequirement.

SWIR Anomaly Detection

In a related study, we have discovered that water-va-por absorption lines in the SWIR are exploitablesources of contrast that enable us to distinguish be-tween natural and man-made objects [5]. Man-madeobjects, particularly paints and plastics, tend to be hy-drophobic; they are designed to repel water to preventrust, corrosion, and degradation. Natural materials,on the other hand, tend to be hydrophilic; they retainwater because they are either porous (in the case ofrocks and soils), or living (in the case of vegetation).Consequently, natural objects like rocks and foliagetypically absorb more radiation in the 1.4-µm and1.9-µm water-vapor absorption lines than do man-made objects. By taking a simple ratio of two broadSWIR spectral bands, one within a water-vapor ab-sorption line and one outside a water-vapor absorp-tion line, we can achieve good anomaly-detectionperformance and distinguish man-made objects fromnatural objects.

We performed an outdoor experiment using sur-face-scattered inert land mines to test this anomaly-detection technique. Five mines and a calibrationpanel were placed in a scene that contained naturalclutter such as grass, rocks, sticks, and plants, asshown in Figure 11. The scene was imaged with andwithout active illumination at a range of 20 m. Theactive illumination was provided by a pair of lasersoperating at 1410 nm and 1600 nm. Three of themines in the scene were positioned such that whenactive illumination was not used, they were eitherpartially or completely shadowed by plants and rocks.The other two mines were completely exposed.

Figure 12 shows a simple band ratio (dividing oneimage by the other on a pixel-by-pixel basis) of the1410-nm image to the 1600-nm image for the scenetaken with and without active illumination. Man-made materials tend to have larger band-ratio values(brighter pixels in the image), while natural materialstend to have smaller band-ratio values (darker pixelsin the image). A band-ratio threshold value was cho-sen such that at least one pixel on each of the targets

Spectral resolution (nm)

0 20 40 60 80 100 120

Fals

e-al

arm

rat

e

10–1

10–2

10–3

10–4

10–5

Ambient, A

Ambient, B

Active, A

Active, B

FIGURE 10. Per-pixel false-alarm rate for detecting mine-likeobjects (plastics A and B) in the scene from Figure 5. Theuse of active illumination reduced the false-alarm rate byabout two orders of magnitude.



was detected. The pixels in each image exceeding thethreshold are denoted in yellow. While the targets aredetectable by using this two-band technique withoutactive illumination, the number of false alarms in theactively illuminated scene is clearly lower. In Figure

13 a horizontal profile taken across the position ofone of the mines illustrates the difference in contrastbetween the two data sets.

We explored this reduction in false alarms by ex-amining the cumulative probability distributions ofthe band-ratio values in the targets and in the back-ground. The first step in calculating the distributionswas subtracting a wide area (20 × 20 pixels) median-filtered version of the band-ratio image from itself toeliminate a gradient across the scene that was causedby variations in the spatial illumination pattern of thetwo lasers. The distribution of the background pixelswas then calculated from all the pixels in the band-ra-tio image, excluding the targets and the calibrationpanel. The target distributions were calculated sepa-rately for each target.

Figure 14 shows the cumulative probability distri-butions of band-ratio values for the scene imaged firstwithout active illumination and then imaged with ac-tive illumination. By looking at these cumulativeprobability distributions, we can compare the per-centage of target pixels exceeding the band-ratiothreshold (detections) to the percentage of back-ground pixels exceeding the band-ratio threshold(false alarms). For example, suppose we want to set aband-ratio threshold value for each scene such that wedetect at least 60% of the pixels on each target. For

FIGURE 11. Photograph of the imaged scene comprisingfive inert land mines amidst clutter of rocks, sticks, and veg-etation. White circles indicate target positions and the blacksquare indicates the position of the calibration panel.

(a) (b)

FIGURE 12. 1410-nm/1600-nm band ratios, using data taken (a) without active illumination and (b) with activeillumination. Circles indicate target positions. Yellow pixels have values that exceed the band-ratio thresh-old. The positions of the horizontal profiles shown in Figure 13 are indicated by a dashed white horizontalline on the band-ratio images.



the scene imaged without active illumination, thisconstraint leads to a band-ratio threshold of 0.15, asindicated by the dashed gray vertical line in Figure14(a). At this threshold, 6% of the background pixelsexceed the threshold and are considered false alarms.For the actively illuminated scene, the band-ratiothreshold necessary to detect at least 60% of the pix-els on each target is 0.13, which corresponds to only0.1% of the background pixels exceeding the thresh-old, an improvement factor of 60.

It is important to note that these numbers are con-

servative estimates. Our targets ranged in size from280 cm2 down to 25 cm2. For the smallest mines,given the resolution of our camera, we had only about4 pixels on target. The odds of each target pixel fallingexactly within a pixel on the sensor were quite small,which means that there were few, if any, “pure” pixelson the smaller targets. This effect was exacerbated bythe fact that the lens on our camera was not achro-matic in the SWIR, which caused a difference in thefocus between the two bands. When the spectral con-tent of a given pixel is mixed between target and

FIGURE 13. Horizontal profiles taken across the same vertical position (indicated by dashed white lines in theband-ratio images shown in Figure 12) taken (a) without active illumination and (b) with active illumination. Thecentral portion of the mine across which the profiles are taken is made of a material different from the outer por-tion. The dip in ratio values seen in (b) may be caused by a spectral feature present in the mine’s inner materialbut not present in its outer material.

FIGURE 14. Cumulative probability distribution curves for each target and the background in a scene imaged(a) without active illumination and (b) with active illumination. The cumulative probability distributions for the ac-tive illumination show greater separability between the targets and the background.

0

0.5

1.0

1.5

2.0

Ban

d ra

tio (1

.4

m/1

.6

m)

0

0.5

1.0

1.5

2.0

Ban

d ra

tio (1

.4

m/1

.6

m)

µµ

µµ

Pixel number

Without active illumination With active illumination

Pixel number

(a) (b)

BackgroundTarget 1Target 2Target 3Target 4Target 5

0

20

40

60

80

100

–0.5 0 0.5 1.0 1.5Band-ratio value

0

20

40

60

80

100

–0.5 0 0.5 1.0 1.5

BackgroundTarget 1Target 2Target 3Target 4Target 5

Band-ratio value

Per

cent

of p

ixel

s ex

ceed

ing

the

band

-rat

io th

resh

old

Per

cent

of p

ixel

s ex

ceed

ing

the

band

-rat

io th

resh

old

Without active illumination With active illumination

Detections Detections

False alarms



background, the band ratio will be smaller. Giventhese factors, the performance increase due to activeillumination could potentially be greater.

Summary

Multispectral and hyperspectral imaging continues togrow and evolve as a powerful remote sensing tool.But relying on the sun for illumination introduces anadditional source of uncertainty that can impact theopportunities for collecting data as well as the utilityof the data. Controlling the illumination source canreduce some of these uncertainties and also offer ad-ditional benefits. Tactical applications especially willbenefit from the ability to operate at night, and hav-ing the illumination source coincident with the im-ager reduces shadowing within the target scene. Inboth laboratory and outdoor measurements we haveshown that the reduced shadowing results in in-creased contrast and reduced false-alarm rates. Activeillumination also provides the opportunity to use po-larization and fluorescence features in conjunctionwith the spectral data to improve detection capability.

While the research summarized in this article hasexplored the phenomenology and demonstratedproof-of-concept approaches for active spectral imag-ing, the promise shown does motivate a number ofmore detailed follow-on questions. In particular,questions come to mind with regard to the sensitivityof active spectral imaging to the number, width, andlocation of spectral bands, with target detection andidentification performance as quantitative metrics.Also, the performance gains from the addition ofthree-dimensional imaging or polarization sensitivityare areas worthy of further investigation. This addi-tional research in developing more quantitative per-formance estimates and comparisons is best pursuedin the context of specific target materials and detec-tion scenarios, and, we hope, will be the subject ofour future work.

Acknowledgments

This program has been very much a team effort, andwe would like to acknowledge the contributions ofAmy Newbury, Mrinal Iyengar, Hsiao-hua Burke,Bernadette Johnson, Bert Willard, Gary Swanson,and Herb Barclay to the work discussed.

R E F E R E N C E S1. A.B. Gschwendtner and W.E. Keicher, “Development of Co-

herent Laser Radar at Lincoln Laboratory,” Linc. Lab. J 12 (2),2000, pp. 383–396.

2. R.G. Reeves, ed., Manual of Remote Sensing, Vol. 1 (AmericanSociety of Photogrammetry, Falls Church, Va, 1975).

3. R.M. Heinrichs, B.F. Aull, R.M. Marino, D.G. Fouche, A.K.McIntosh, J.J. Zayhowski, T. Stephens, M.E. O’Brien, andM.A. Albota, “Three-Dimensional Laser Radar with APDArrays,” SPIE 4377, 2001, pp. 106–117.

4. B. Johnson, R. Joseph, M. Nischan, A. Newbury, J. Kerekes,H. Barclay, B. Willard, and J.J. Zayhowski, “A Compact,Hyperspectral Imaging System for the Detection of ConcealedTargets,” SPIE 3710, 1999, pp. 144–153.

5. J.J. Zayhowski, “Passively Q-Switched Nd:YAG MicrochipLasers and Applications,” J. Alloys Compd. 303–304, 24 May2000, pp. 393–400.

6. A.B. Newbury, M. Nischan, R. Joseph, M. Iyengar, B. Willard,J. Libby, G. Swanson, B. Johnson, and H. Burke, “Detectionof Manmade Objects,” SPIE 4132, 2000, pp. 126–135.

7. J.R. Schott, Remote Sensing: The Image Chain Approach (Ox-ford University Press, New York, 1997).

8. R. Anderson, W. Malila, B. Maxwell, and L. Reed, IRIA Stateof the Art Reports, SOAR 1, Military Utility of Multispectral andHyperspectral Sensors, Report No. 246890-3-F, EnvironmentalResearch Institute of Michigan, Ann Arbor, Mich. (11 Nov.1994).

9. J.D. Howe, M.A. Miller, R.V. Blumer, T.E. Petty, M.A.Stevens, D.M. Teale, and M.H. Smith, “Polarization Sensingfor Target Acquisition and Mine Detection,” SPIE 4133,2000, pp. 202–213.

10. A.C. Schuerger, G.A. Capelle, J.A. Di Benedetto, C. Mao,C.N. Thai, M.D. Evans, J.T. Richards, T.A. Blank, and E.C.Stryjewski, “Comparison of Two Hyperspectral Imaging andTwo Laser-Induced Fluorescence Instruments for the Detec-tion of Zinc Stress and Chlorophyll Concentration in BahiaGrass (Paspalum Notatum Flugge),” Remote Sens. Environ. 842003, pp. 572–578.

11. M.L. Nischan, A.B. Newbury, R. Joseph, M. Iyengar, B.Willard, G. Swanson, J. Libby, B. Johnson, and H.-H. Burke,“Active Hyperspectral Imaging,” SPIE 4312, 2000, pp. 107–117.



. is a staff member in BiodefenseSystems group, where sheresearches models for im-proved bioaerosol detection.Before joining the Laboratoryin 1997, she worked on ad-vanced color xerography forXerox Corporation in Webster,New York. Rose earned an S.B.degree in electrical engineeringfrom MIT and a Ph.D. degreein electrical engineering andcomputational electromagnet-ics from NorthwesternUniversity.

. is an associate staff member inthe Sensor Technology andSystem Applications group.She helped develop and fieldtest active hyperspectral/multispectral sensors, andcharacterized the performanceof various detector and lasersystems. She also developed anon-line-of-sight ultravioletcommunications test bed andsoftware for distributed net-working applications. Cur-rently, Melissa works on watermonitoring systems forbiodefense. She joined theLaboratory in 1996, aftergraduating from the Universityof Massachusetts, Amherst,with an M.S. degree in as-tronomy. She also holds a B.A.degree in physics fromFranklin and Marshall Collegein Lancaster, Pennsylvania. Sheis a member of Sigma PiSigma: the National PhysicsHonor Society.

. is an associate staff member inthe Laser and Sensor Applica-tions group. Upon joining theLaboratory in 2000, heworked on the wake vortexlaser radar (ladar) system,collecting data on commercialaircraft as well as the V-22Osprey, a military tiltrotoraircraft. Justin has also per-formed data-registrationalgorithm development for 3Dladar systems, as well as systemdesign, assembly, and testingfor the laser vibrometry pro-gram. For the hyperspectralimaging program, he partici-pated in fielding the multispec-tral imager system and theactive multispectral rangeimager. Justin earned B.S. andM.S. degrees in applied phys-ics from Cornell University.

. is a staff member in the SensorTechnology and System Appli-cations group. His primary areaof research has been the devel-opment of remote sensingsystem-performance modelsfor geophysical parameterretrieval and object detectionand discrimination applica-tions. After joining LincolnLaboratory in 1989, he workedon performance analyses forballistic missile discriminationproblems. He then developedanalysis models supportingweather-satellite instrumentdevelopment. In particular, hedeveloped models to study theaccuracy of proposed sensorsystems in retrieving the atmo-spheric vertical temperatureand water-vapor profiles.Recently, he has developedmodeling approaches for theprediction of the system per-formance of hyperspectralimaging systems. He is asenior member of the Instituteof Electrical and ElectronicEngineers (IEEE), and amember of the AmericanGeophysical Society, theAmerican MeteorologicalSociety, and the AmericanSociety for Photogrammetryand Remote Sensing. He is anassociate editor of the IEEETransactions on Geoscience andRemote Sensing, and chairs theBoston Chapter of the IEEEGeoscience and Remote Sens-ing Society. He received hisB.S., M.S., and Ph.D. degreesin electrical engineering fromPurdue University.

active spectral imagingactive spectral imaging melissa l. nischan, rose m. joseph, justin c. libby,...

Documents