automated detection of chemical vapors by pattern recognition analysis of passive multispectral...

1082 Volume 56, Number 8, 2002 APPLIED SPECTROSCOPY0003-7028 / 02 / 5608-1082$2.00 / 0q 2002 Society for Applied Spectroscopy

Automated Detection of Chemical Vapors by PatternRecognition Analysis of Passive Multispectral InfraredRemote Sensing Imaging Data

LIN ZHANG and GARY W. SMALL*Center for Intelligent Chemical Instrumentation, Department of Chemistry and Biochemistry, Ohio University, Athens, Ohio 45701

Pattern recognition methods are developed for the automated in-terpretation of passive multispectral imaging data collected from anairborne platform. Through the use of an infrared line scannerequipped with 14 spectral bandpass � lters, passive infrared imagesare collected of an ammonia plant within a nitrogen fertilizer fa-cility. Piecewise linear discriminant analysis is used to implementan automated algorithm for the detection of scene pixels that cor-respond to chemical vapor signatures. A separate classi� er is usedto detect the presence of hot carbon dioxide (CO2) within the im-ages. In the assembly of training and prediction data for the devel-opment of both classi� ers, the K-means clustering algorithm is usedtogether with knowledge of the site to assign pixels to the plume/nonplume and CO 2/non-CO2 categories. The effects of temperaturevariation within the imaged scene are removed from the datathrough the use of an algorithm for separating the contributions oftemperature and emissivity to the Planck equation. Averaged acrossfour data runs containing a total of 3.5 million pixels, the resultingdiscriminants are observed to detect approximately 91% of theplume pixels while achieving a false detection rate of less than0.01%. The corresponding performance criteria for the CO2 clas-si� er are a successful detection of approximately 94% of the pixelswith a CO2 signature and a false detection rate of less than 0.7%.The robustness of the CO2 classi� er is further enhanced throughthe adoption of a probability-based classi� cation rule.

Index Headings: Remote sensing; Passive infrared; Imaging; Patternrecognition; Temperature emissivity separation.

INTRODUCTION

Remote chemical sensing techniques offer the abilityto acquire chemical information about a sample withouthaving to perform a physical sample collection.1,2 Amongthese techniques, passive infrared measurements have re-ceived signi� cant attention because of the intrinsic selec-tivity of infrared spectroscopy for the determination ofmolecular species and because naturally occurring sourc-es of infrared radiation abound in the environment. Forexample, remote determinations can be based on acquir-ing the infrared emission from hot gases3 or the collectionof absorption information from the intervening atmo-sphere between an infrared sensor and a source of natu-rally occurring background radiance.4

For the detection of speci� c chemical signatures, in-frared remote sensing measurements must have suf� cientspectral selectivity to isolate the characteristic vibrationalfrequencies of target molecules. This spectral selectivitycan be accomplished through either multispectral or hy-perspectral measurements. Hyperspectral sensors resolvethe infrared radiance within the � eld of view (FOV) of

Received 29 October 2001; accepted 20 February 2002.* Author to whom correspondence should be sent.

the instrument into evenly spaced, contiguous spectralresolution elements across a targeted spectral range.These measurements are typically made with portableFourier transform infrared (FT-IR) spectrometers. Earlywork in this � eld was based on the acquisition of rela-tively high resolution spectra (e.g., 0.25 or 0.125 cm21),5

while recent efforts have shown that selective chemicalinformation can also be obtained with spectra measuredat lower resolution (e.g., 8 cm21).6 Somewhat analogousto low-resolution hyperspectral measurements, multispec-tral sensors acquire data for a limited number of discretespectral bands, typically selected by a set of bandpassoptical � lters.

Remote sensing techniques can be imaging or non-imaging in nature and can be applied from ground, air-borne, and spaceborne observation platforms. Advancesin multispectral imaging systems over the past decadehave enhanced their remote sensing capabilities, makingit possible to detect and image chemical vapors in theatmosphere.1,7,8 These systems have potential utility in avariety of environmental monitoring applications inwhich a site needs to be surveyed remotely for the pur-pose of determining the presence of one or more targetedchemical species.

Previous work in our laboratory on passive infraredremote sensing has focused primarily on chemical vapordetection from nonimaging hyperspectral sensors posi-tioned on the ground.4,9,10 In the work described here,automated detection algorithms are developed for usewith multispectral imaging measurements made from anaircraft platform. The system is used to acquire two-di-mensional images of a nitrogen fertilizer facility across14 infrared bands. In the � rst part of the work, a classi� eris built to identify scene pixels as ‘‘plume’’ or ‘‘non-plume’’ corresponding to the presence or absence ofchemical vapor signatures, respectively. The second partof the work attempts to evaluate the chemical selectivityof the data by building a classi� er for hot carbon dioxide(CO2). The robustness of both classi� ers is enhancedthrough the use of an algorithm to separate temperatureand emissivity information in the imaging data.

EXPERIMENTAL

Data Collection. The imaging system used in thisstudy was an RS-800 multispectral infrared line scanner(Raytheon TI Systems, McKinney, TX) mounted in adownward-looking position in a modi� ed Douglas-Curtis3 aircraft. During the data collection, the aircraft � ewwith a nominal airspeed of 90 knots at altitudes of either2200 ft. (671 m) or 1100 ft. (335 m). Through an across-

APPLIED SPECTROSCOPY 1083

TABLE I. Description of bandpass � lters.

Bandnumber

Band center(mm)/(cm21)

Band width(mm) Purpose

1234567

9.67/1033.510.42/959.28.73/1145.0

10.76/929.211.36/880.410.66/938.59.48/1054.5

0.720.220.830.210.970.400.76

CH3OHNH3 #1SO2

NH3 #2backgrounda

SF6

hot CO2

89

1011121314

9.33/1071.94.44/2252.34.24/2358.53.41/2930.04.11/2428.43.88/2576.05.33/1875.8

1.040.110.090.082.760.100.35

backgrounda

red CO2

blue CO2

HClbackgrounda

backgrounda

NO

a Background � lters are designed to characterize the infrared back-ground radiance in the 8–12 and 3–5 mm spectral regions.

FIG. 1. Contrast-enhanced infrared image for run 20 on day 2 based on the radiance values from channel 8. Structures with a high radiancetemperature appear white in this image. The locations of the three stacks of primary interest are labeled.

track scanning procedure, the imager scanned the groundalong scan lines perpendicular to the � ight line. The basicdesign of the system is that of an object plane one-di-mensional scanner coupled with an infrared imager. Therefractive scan element is a double-dove prism made ofgallium arsenide. This is rotated at a scan rate of 30 Hz,scanning the FOV of an array of photoconductive Hg:Cd:Te (MCT) detectors across a 3608 total FOV. Of this,an unobstructed 608 FOV is sampled. The � nal image is

of the format ‘‘samples 3 lines,’’ where the number ofsamples is the number of image pixels taken along eachscan line and the number of lines is the number of scanlines along the � ight line. Fourteen infrared bandpass � l-ters were coupled to the detector array and spatial scan-ner. Table I provides the band centers, widths, and pur-poses for the 14 � lters.

Radiometric calibration was performed to transformthe digitized detector responses into radiance units. Foreach scan line, the digital responses of two internal black-body reference sources were recorded. The blackbodytemperatures were set to span the anticipated ground ra-diances, typically 25 and 45 8C. The radiance values ofeach pixel along a scan line were calculated by a linearinterpolation between the digital responses of the twoblackbody sources. This calculation was performed in-dependently for each spectral channel of the detector. Theradiance of the two reference sources was measured bya transfer calibration conducted daily using an external,NIST-traceable blackbody. The details of this calibrationprocedure have been described previously.11

The digital spectral image is inherently misregisteredbecause of the limitations of across-track scanning. Eachdetector element views a slightly different ground loca-tion. The digital image cube was spatially registered byuse of a polynomial warping algorithm developed by theinstrument manufacturer. In addition, registration errors

1084 Volume 56, Number 8, 2002

FIG. 2. Passive FT-IR difference spectra collected from the ground with the stacks viewed against a low-angle sky background. The 3–5 and 8–12 mm atmospheric transmission windows are displayed. (Upper: primary reformer; middle: tail-end stack; bottom: urea � are stack.)

related to the pitch and yaw of the aircraft were correctedby use of � ight data recorded during the data runs. Aftercalibration and registration, the resulting image cube con-tained radiance values for each of the 14 bands listed inTable I.

An ammonia plant within a nitrogen fertilizer facilitywas investigated in this study. Three major emissionsources investigated were: (1) nitric acid tail-end stackreleasing ammonia (NH 3) and nitric oxides (NO x); (2)urea � are stack releasing NH 3 and carbon dioxide (CO2);and (3) primary reformer stack releasing CO2, carbonmonoxide (CO), and trace amounts of NH3 and methanol(CH3OH).

A series of experiments were performed on three days.Images used in this work were run 2 (1300 samples and913 lines) on day 1, run 8 (1300 samples and 691 lines)on day 2, run 17 (1300 samples and 611 lines) on day 2,and run 20 (1300 samples and 500 lines) on day 2. Run20 on day 2 was recorded at an altitude of 1100 ft., whilethe remaining images were measured from an altitude of2200 ft. These images are typical of the data collectedover the three days. Figure 1 is a contrast-enhanced im-age for run 20 on day 2 obtained by application of alinear stretching function to the radiance values in chan-nel 8. The three stacks of interest are labeled in the � gure.

The stacks were also monitored from ground level withtwo Midac M2400 Series passive FT-IR spectrometers(Midac, Inc., Irvine, CA). These systems were equippedwith cryogenically-cooled MCT and indium antimonide(InSb) detectors to allow data collection in the 8–12 and3–5 mm atmospheric transmission windows, respectively.The stack emissions were viewed against a low-angle skybackground. Data acquisition used the MIDCOL software

package.12 Single-scan, single-sided 1024-point interfer-ograms were collected with the MCT detector. Interfer-ogram points were sampled every eight zero-crossings ofthe HeNe reference laser, yielding a maximum spectralfrequency of 1975 cm21 and a nominal point spacing of4 cm21. With the InSb detector, single-scan, single-sided2048-point interferograms were collected. Points weresampled every four zero-crossings of the reference laser,yielding a maximum spectral frequency of 3950 cm21 anda nominal spectral point spacing of 4 cm21. The Fourierprocessing steps used with the interferogram data includ-ed triangular apodization and Mertz phase correction.

Data Analysis. All calculations were performed onSilicon Graphics Indigo2 IMPACT workstations (SiliconGraphics, Mountain View, CA) under the Irix operatingsystem (version 6.5). ENVI (version 3.2, Research Sys-tems, Boulder, CO) was used for the initial display ofimages. Piecewise linear discriminant analysis (PLDA)training was performed on this system with original soft-ware written in FORTRAN 77. K-means clustering,PLDA prediction, additional processing, and graphingwere performed under MATLAB (version 5.2b, TheMathWorks, Inc., Natick, MA).

RESULTS AND DISCUSSION

Spectral Analysis of Stack Emissions. Spectra col-lected from the ground with the two FT-IR spectrometerswere inspected to evaluate the chemical informationavailable from the three targeted stacks. Figure 2 plotsdifference spectra in uncalibrated intensity units for eachstack and spectral region. The difference spectra wereobtained by subtracting a background spectrum collected


upwind of the stack to help remove the background ra-diance of the atmosphere and the spectrometer.

The spectrum of the reformer stack in Fig. 2 containsthe prominent blue and red spikes of hot CO2 centerednear 2390 and 2250 cm21, respectively. These featurescorrespond to the wings of a broad emission band, thecenter of which is removed through absorption by am-bient CO2 present in the intervening atmosphere betweenthe stack and spectrometer. In the 8–12 mm region, thespectral pro� le is complex, although the signatures ofCO2 and water vapor (H2O) are apparent.

The symmetric (929 cm21) and antisymmetric (964cm21) H–N–H deformation bands of NH3 are apparent inthe spectrum of the tail-end stack along with the rota-tional � ne structure associated with these bands. Funda-mental vibrations of nitrous oxide (N 2O) are also ob-served at 1285 and 2223 cm21.

The spectrum acquired from the urea � are stack iscomplex in the 8–12 mm region, although the NH 3 andN2O bands are apparent. In the 3–5 mm region, the CO2

blue spike is clearly observed along with a compositeband formed from the CO2 red spike at 2250 cm21 andthe N2O band at 2223 cm21.

Temperature Effects on Measured Radiance Val-ues. The spectral radiance of the ith pixel at the jth chan-nel in the calibrated image data follows Planck’s func-tion:

e ci j 1R 5 (1)i j 5 c /l T2 j il p (e 2 1)j

where R ij is the spectral radiance in units of W m23 sr21,eij is the emissivity of the ith pixel at the jth channel, l j

is the center wavelength of the jth channel (m), T i is theabsolute temperature of the ith pixel in the scene surface(K), c1 is the � rst radiation constant (1.190 3 10216

W m 2sr2 1), and c 2 is the second radiation constant(0.0143879 mK).

Emissivity is a factor that describes how ef� ciently anobject radiates energy compared to a blackbody. By def-inition,

Me(l) 5 (2)

M blackbody

where M and M blackbody are the radiances produced atwavelength l by the object and a blackbody, respectively,at the same temperature. Except for materials which actas blackbodies or graybodies, the emissivity of other spe-cies changes with wavelength. The resulting emissivityspectra provide a characteristic pattern that can be usedto differentiate chemical species.

Equation 1 shows that the radiance encodes the tem-perature of the scene as well as its chemical composition.The dependence of radiance on temperature makes it dif-� cult to discriminate the thermal and spectral componentsof infrared radiances. This implies that any chemical sig-nature will be affected by the underlying temperature in-formation. Furthermore, because temperature variationwill be encountered from day to day and site to site, itwill be dif� cult to develop a robust classi� er based onradiance data unless the temperature variation is system-atically included in the development of the classi� er.Thus, temperature and emissivity separation (TES) is an

important step before the analysis of radiance data. Im-plementing an effective TES algorithm is not a trivialtask, however, because the problem is nonderterministic.If the radiance is measured in n spectral channels, therewill always be n 1 1 unknowns, i.e., n emissivities (oneper channel) plus an unknown surface temperature. Asecond complicating factor is the nonlinear relationshipin Eq. 1 between radiance and surface temperature.

Several TES algorithms have been developed on thebasis of different assumptions to constrain the extra de-gree of freedom.13–15 One approach, termed the alphaemissivity method,13,14 employs Wien’s approximation ofPlanck’s function in which the ‘‘21’’ term in the denom-inator of Eq. 1 is neglected. This makes it possible tolinearize the approximation with a logarithmic transfor-mation. For a temperature of 300 K and a wavelength of10 mm, Wien’s approximation results in errors in the ra-diance value of less than 1%.

Taking the natural logarithm of the radiance usingWien’s approximation we obtain:

c 2ln R 5 ln e 1 ln c 2 5 ln l 2 ln p 2 (3)i j i j 1 j l Tj i

Equation 3 is then multiplied by lj in order to separatethe l and T i terms:

c 2l ln R 5 l ln e 1 l ln c 2 5l ln l 2 l ln p 2 (4)j i j j i j j 1 j j j T i

By calculating the mean of the equation set for channelsj 5 1 to n, we obtain:

n n n1 1 ln c1l ln R 5 l ln e 1 lO O Oj i j j i j jn n nj51 j51 j51

n n5 ln p c 22 l ln l 2 l 2 (5)O Oj j jn n Tj51 j51 i

Subtracting Eq. 5 from Eq. 4 yields:

n1l ln R 2 l ln ROj i j j i jn j51

n15 l ln e 2 l ln e 1 l ln cOj i j j i j j 1n j51

n nln c 512 l 2 5l ln l 1 l ln lO Oj j j j jn nj51 j51

nln p2 l ln p 1 l (6)Oj jn j51

The temperature effect has been removed from this dif-ference expression since the temperature contributesequally to each channel. Different forms of alpha emis-sivity have been reported by moving terms from the leftside to the right side in Eq. 6.13,14 In this study, the fol-lowing form was used:


FIG. 3. Clustering images for run 20 on day 2 (see Fig. 1 for radiance image). The top and bottom images are based on alpha emissivity andradiance data, respectively. On the basis of knowledge of the site, plume pixels appear in the light blue cluster in the top � gure and in the lightblue and orange clusters in the bottom � gure. The removal of temperature information by the alpha residual calculation is apparent in the primaryreformer region near pixel (240, 150). The pixels in this region are placed in two clusters when the radiance data are used, thereby differentiatingpixels near the stack exit � ue where the temperature is greater. This interfering temperature effect is removed by the alpha emissivity calculation,and the resulting pixels are found in a single cluster.


FIG. 4. Discriminant score images for run 20 on day 2 (top) and run 2 on day 1 (bottom) produced by the plume/nonplume classi� er. A subsetof the pixels in run 20 on day 2 formed the training set used to compute the piecewise linear discriminant. No pixels in run 2 on day 1 were usedin training the classi� er. In the upper � gure, approximate coordinates for the primary reformer, tail-end stack, and urea � are stack are (240, 150),(60, 110), and (250, 200), respectively. The corresponding coordinates in the lower � gure are (220, 290), (120, 250), and (230, 325). The origin islocated at the top left of each plot.


n n1 ln pa 5 l ln e 2 l ln e 2 l ln p 1 lO Oi j j i j j i j j jn nj51 j51

n n1 ln c 15 l ln R 2 l ln R 2 l ln c 1 lO Oj i j j i j j 1 jn nj51 j51

n51 5l ln l 2 l ln lOj j j jn j51

n15 l ln R 2 l ln R 1 k (7)Oj i j j i j jn j51

where aij is the alpha emissivity for pixel i at channel jand k j is a channel-dependent constant which can be cal-culated from the center wavelength of each channel andthe � rst radiation constant (c1). Equation 7 illustrates thatthe alpha emissivity encodes the variation in logarithmi-zed emissivity for a given channel about the mean lo-garithmized emissivity of the n channels rather than thevariation of temperature and emissivity.

Data Pretreatment. First, channel selection was per-formed on the basis of a study of signal-to-noise (S/N)ratios of the radiance values. The S/N ratio of each chan-nel was calculated by assuming that a pixel and its sur-rounding 8 pixels are effective replicates in terms of thescene viewed. The mean and standard deviation can becalculated from the set of nine responses centered on agiven pixel (except for those pixels lying on the boundaryof the image). The S/N ratio can then be approximatedas the mean divided by the standard deviation. In thiscase, 1% of the pixels were randomly picked from animage and the median of the S/N ratios was used as anindex to evaluate channel performance. These calcula-tions revealed that the S/N ratios of channels 1–8 weresigni� cantly higher than the corresponding values forchannels 9–14. Although the S/N ratios of channels 9 and10 were relatively low (S/N , 50) compared to channels1–8 (95 , S/N , 216), the corresponding � lter positionswere designed to isolate hot CO2 emission bands. Giventhe prominence of the hot CO2 signatures in Fig. 2, thesechannels were retained and subsequent work employedchannels 1 to 10.

The imaging data sets were very large and taxed theavailable computational resources. Spatial resolution wassacri� ced to improve the ef� ciency of the calculations.The number of samples was reduced to Ä and the numberof lines was reduced to ½ by taking every third or everyother pixel, respectively. For run 2 on day 1, the reducedimage contained 434 samples and 456 lines. For run 8on day 2, the reduced image had 434 samples and 345lines. For run 17 on day 2, the reduced image consistedof 434 samples and 305 lines, and for run 20 on day 2,the reduced image contained 434 samples and 250 lines.In the following discussion, reduced resolution data setswere used unless explicitly mentioned.

Cluster Analysis to Identify Plume Pixels. The � rststep of this work was to identify the plume pixels foreach image. For this discussion, plume pixels are de� nedas those pixels in which the dominant information cor-responds to chemical vapor released from the variousstacks at the facility. Since the chemical composition ofa plume pixel is signi� cantly different from that of anonplume pixel, an unsupervised clustering algorithm

can ful� ll this task without any category knowledge. TheK-means algorithm was used in this work.16

The K-means algorithm is an unsupervised clusteringalgorithm that attempts to group a set of N patterns, x1,x2, . . . , xN, into disjoint clusters C1, C2, . . . , C k withouta priori category information. In the work described here,a pattern is a 10-dimensional vector formed from the al-pha emissivities of channels 1 to 10 corresponding to onepixel. The K-means algorithm minimizes J se, the sum ofsquared distances of the patterns to their correspondingcluster means:

K Nk

(k) 2J 5 \ x 2 m \ (8)O Ose i kk51 i51

where is the ith of the N k patterns belonging to cluster(k)x i

C k and m k is the mean of cluster C k:N (k)k x im 5 (9)Ok Ni51 k

Each possible partitioning of the N patterns into K clus-ters produces a value of J se.

The K-means algorithm is an iterative procedure andthus it cannot guarantee convergence to the global min-imum in one run. As a remedy, the algorithm was run� ve times with random initial partitions and the result ofthe run that gave the minimum value of J se was used.

To facilitate the interpretation of the clustering results,images were formed from the cluster memberships of thepixels. The K-means algorithm assigns each pixel to acluster with a membership integer such as 1 or 2 to in-dicate the presence of the pattern corresponding to thatpixel in cluster 1 or 2. In this study, for a given clusteringresult, the membership integers were reconstructed backinto a matrix of the size of the data image matrix. Aclustering color image was subsequently generated by as-sociating each membership integer with a distinct color.

An inevitable problem encountered with most cluster-ing algorithms is that the number of clusters, K, must bede� ned prior to the clustering process. In this work, atrial-and-error approach was employed to choose K. Thiswork was based on the assumption that plume and non-plume pixels should have signi� cantly different signa-tures. Thus, the plume pixels were anticipated to form adistinct cluster. For the image data sets from each run,the clustering was performed with an initial value of K5 3. Next, K was increased by one, the clustering processwas repeated, and the resulting clustering color imagewas visually inspected. On the basis of our knowledge ofthe facility, if the stack pixels formed a stable cluster, theprocess stopped.

To compare the performance with radiance and alphaemissivity data, the clustering was performed separatelywith both types of input data. For all four data sets, thestack pixels formed distinct clusters in the clustering col-or images as the number of clusters increased to six usingeither the radiance or alpha emissivity values. Therefore,the results from K 5 6 were used in the following study.

Figure 3 presents clustering images for run 20 on day2 for both alpha emissivity (top) and radiance (bottom)values. The other data runs produced similar clusteringresults. Visual comparison between the clustering imagesfor the radiance and alpha emissivity values reveals that


TABLE II. Classi� cation results for plume classi� er.

Data runPlume classi� cation

percentageFalse detection

percentage

Run 20 on day 2Run 8 on day 2Run 17 on day 2Run 2 on day 1

356/367 5 97.0%157/188 5 83.5%185/205 5 90.2%129/137 5 94.2%

13/108 133 5 0.01%15/149 542 5 0.01%0/132 165 5 0

20/197 767 5 0.01%

in the regions of the stacks (see Fig. 1), the alpha emis-sivity data produce a single cluster, whereas the radiancedata typically produce two clusters. A common obser-vation about hot gas plumes is that they tend to coolquickly. It was hypothesized that the clustering resultsarise from the fact that the radiance values directly en-code temperature information. Thus, the clustering algo-rithm split the plume pixels into two clusters, i.e., thepixels corresponding to the hot gas in the vicinity of thestack exit � ue and the surrounding pixels in which thegas had cooled to a lower temperature. This result alsoprovides evidence that the alpha emissivity values pro-vide a convenient means of removing the temperaturesignature from the image. On the basis of these results,the alpha emissivity data were used in all further work.

Development of a Classi� er for Plume Detection. Toautomate the analysis of infrared remote sensing data, avariety of classi� cation algorithms can be used. Recentstudies have included the use of cross-correlation meth-ods17 and feed-forward arti� cial neural networks.6 In thisstudy, a numerical pattern recognition technique, PLDA,was used to build a classi� er for plume detection. PLDAis an extension of linear discriminant analysis (LDA) forsolving nonlinear classi� cation problems. LDA attemptsto separate patterns into different classes by building mul-tidimensional linear surfaces called discriminants .18

PLDA is based on LDA solutions and uses multiple lin-ear discriminants to form a piecewise approximation ofa nonlinear separating surface.19,20 During the trainingprocess, patterns with known categories (i.e., plume andnonplume) were used to decide the placement of theselinear surfaces. In this work, initial approximations of thediscriminants were obtained with the Bayes classi� cationalgorithm19,20 and discriminants were further optimized bysimplex optimization. Application of the piecewise lineardiscriminant to an unknown pattern produces a discrim-inant score, a signed distance of the pattern to the nearestclassi� cation boundary. In this work, the data were or-ganized such that positive discriminant scores indicatedplume pixels.

Training and prediction data sets were assembled withthe pixels identi� ed from the clustering results. For eachdata run, an unambiguous stack cluster was identi� ed onthe basis of knowledge of the site. All pixels included inthe stack cluster were taken as plume pixels and the pix-els from the other � ve clusters were combined as non-plume pixels. For run 2 on day 1, 137 and 197 767 pixelswere identi� ed as plume and nonplume pixels, respec-tively. For run 8 on day 2, 188 and 149 542 pixels wereassigned to the plume and nonplume categories, respec-tively. For run 17 on day 2, 205 and 132 165 pixels wereidenti� ed as plume and nonplume pixels, respectively.For run 20 on day 2, the plume and nonplume groupscontained 367 and 108 133 pixels, respectively.

The data set for run 20 on day 2 was used as thetraining set for calculation of the piecewise linear dis-criminant. All 367 plume pixels and 9633 randomly pick-ed nonplume pixels were combined to form a training setof 10 000 patterns. After applying PLDA to the trainingset, a classi� er consisting of two individual linear dis-criminants was obtained. These two discriminants sepa-rated 357 of the 367 plume pixels for the training set(97.28%). Next, the classi� er was used to perform pre-

dictions on all four data sets. Two metrics were used toevaluate the performance of the classi� cation, i.e., theclassi� cation percentage and the false detection percent-age. The classi� cation percentage is computed from theratio of the number of correctly identi� ed plume pixelsto the total number of plume pixels. The false detectionpercentage is based on the ratio of the number of non-plume pixels identi� ed to be plume pixels to the totalnumber of nonplume pixels.

Table II lists the classi� cation and false detection per-centages for all four data sets. These results are satisfac-tory. Note that for the training data set, run 20 on day 2,the number of correctly detected plume pixels is 356,which is one less than the training result, 357. This in-consistency resulted from variation in calculation preci-sion between the MATLAB and FORTRAN code usedto perform the prediction and training calculations, re-spectively.

For imaging work, numerical values are insuf� cient toillustrate the results. To facilitate the interpretation of theclassi� cation results, a method was proposed to constructa discriminant score color contrast image for the classi-� cation results of each data set. The standard color mapused in constructing the image ranged from 0 to 63, cor-responding to a range of color from dark blue (0) to red(63). Two linear stretching functions were constructed tomap a discriminant score value, x, to a correspondingscaled value, y. Assume the maximum and minimum val-ues among the discriminant scores of the training set aresmax and smin, respectively.

If x . s , x 5 s (10)max max

If x , s , x 5 s (11)min m in

(high 2 low )1 1If x . t, y 5 (x 2 t) 1 low (12)1s 2 tmax

(high 2 low )2 2If x , t, y 5 (x 2 t) 1 low (13)2s 2 tmin

where low1 5 44.5, high1 5 63, low2 5 18.5, and high2

5 0, and a threshold value of t 5 0 was used unlessexplicitly mentioned.

Through application of Eqs. 10–13, a new color mapbetween discriminant score value and color was estab-lished. Pixels classi� ed as ‘‘plume’’ (positive discrimi-nant scores) are displayed in orange through red, whilepixels classi� ed as nonplume (negative discriminantscores) are blue. Figure 4 displays discriminant score im-ages for run 20 on day 2 (top) and run 2 on day 1 (bot-tom). Similar images were obtained for the other two dataruns. These results show that the classi� er has identi� edthe primary reformer, tail-end stack, and urea � are stackcorrectly. As detailed previously, the training set was


TABLE III. Classi� cation results by hot CO2 classi� er for regionsof interest.

Data runCO2 classi� cation

percentageFalse detection

percentage

Run 20 on day 2Run 8 on day 2Run 17 on day 2Run 2 on day 1

928/932 5 99.6%582/607 5 95.9%573/578 5 99.1%517/636 5 81.3%

368/28 714 5 1.3%114/28 067 5 0.4%201/29 842 5 0.7%60/22 800 5 0.3%

FIG. 5. Cumulative probability plot for discriminant scores of CO2 pixels from run 8 on day 2. 95.1% (577) of the 607 CO2 pixels have discriminantscores less than or equal to 10.5 3 10210. The dashed line corresponds to a cumulative probability of 95.1%.

drawn entirely from a subset of run 20 on day 2. Thefact that the classi� er operates well with data from a dif-ferent day is an encouraging result. Also note that on theleft of the primary reformer region in these images, thereare several small clusters of plume pixels. These corre-spond to a number of small vent stacks known to existat the site.

Development of a Classi� er for Detection of HotCO2. In the next study, the chemical selectivity of theimaging data was evaluated by building a hot CO2 clas-si� er. The goal of this classi� er was to discriminate CO2

against all other pixels including both nonplume pixelsand other plume pixels that do not contain the signatureof hot CO2. The � rst step was to identify the hot CO2

pixels. As shown in Fig. 2, the ground FT-IR spectra ofthe primary reformer and urea � are stacks both containobvious bands from hot CO2, thereby providing solidproof that these stacks emitted CO2. The ground FT-IRspectrum of the nitric acid tail-end stack contains no ob-vious CO2 signature. Since the urea � are stack also hasa signi� cant N2O signature, only plume pixels from theprimary reformer stack were used as ‘‘pure’’ CO2 pixels.In order to obtain as many CO2 pixels as possible, thefull resolution images were used. To circumvent the com-putational dif� culty caused by the large amount of data,

rectangular regions of interest (ROI) were selectedaround the primary reformer for all four runs. By a step-wise increase in the number of clusters from three to � ve,stable plume clusters were observed after the number ofclusters reached four. The results of K-means clusteringof the alpha residual data into four clusters were used inthe following work.

The clustering images obtained were similar to thosedisplayed in Fig. 3. These images revealed that somescattered pixels outside the continuous entity of theplume pixels were clustered into the same class as theplume pixels. However, since no FT-IR spectra wereavailable to help diagnose the chemical components ofthese scattered pixels, they were excluded from the CO2

class in the following study. As before, run 20 on day 2was used as a training set. The training data for the CO2

class consisted of 932 plume pixels, while the non-CO2

class contained 9068 randomly picked nonplume pixelsfrom within the ROI. After the PLDA training step, aclassi� er consisting of two individual linear discriminantswas obtained. The classi� er correctly separated 929 outof the 932 CO2 pixels in the training set (99.68%).

An initial evaluation of the classi� er was performed bypredicting the ROIs of the full resolution data with theaid of category information from the clustering results.Table III lists the classi� cation results for the ROIs foreach run. The classi� cation percentages for the CO2 classare satisfactory. While the false detection percentages arehigher than those achieved in the previous study, the av-erage false detection is still less than 0.7%. The higherfalse detection percentages are caused by the artifact thatmany scattered pixels outside the continuous entity of theplume pixels were not counted into the active class. Theclassi� cation percentage of the CO2 class for run 2 on


FIG. 6. Probability image plot for run 17 on day 2 (Top: 95% probability; bottom: 80% probability). The rate of false detection decreases as theprobability level is increased. The approximate coordinates of the primary reformer and urea � are stacks in each image are (110, 65) and (130,100), respectively. The origin is located at the top left of each plot.


TABLE IV. Classi� cation results for hot CO2 based on probabili-ties.

Data run

Number of CO2 pixels detected at agiven probability level

95% 90% 85% 80%

Run 17 on day 2Run 2 on day 1

62

1511

2418

3825

day 1 is signi� cantly lower than that of the other runs.The degradation of the classi� er performance on this runmay be due to the fact that this run was performed on adifferent day than the run used to form the training set.A study was performed to locate those pixels not detectedby the classi� er in this run. The result showed that themissed pixels were at the outside boundary of the pri-mary reformer stack.

Next, the PLDA classi� er was used to perform predic-tions on the four reduced resolution data sets. In this case,the classi� cation results were not satisfactory, as a largenumber of false detections were observed outside the re-former ROI. By restricting the training set to pixels nearthe reformer stack, the trained discriminants have notbeen given the ability to recognize other structures asnon-CO2.

This result illustrates the disadvantage of the nonpara-metric nature of the PLDA method, i.e., classi� cationsare either plume or nonplume, with no assigned proba-bilities. One way to improve the robustness of the PLDAresults is to use a probability-based classi� cation basedon empirical probabilities computed from a large dataset.4 An independent data set outside of the training set,run 8 on day 2, was used to assign the probabilities. Fig-ure 5 is the cumulative probability plot for discriminantscores based on the known CO2 pixels from run 8 on day2. For a discriminant score value x, the correspondingempirical cumulative probability was calculated by ratio-ing the number of CO2 pixels in the independent data setwhose discriminant scores are less than or equal to x tothe total number of CO2 pixels in the independent dataset. Thus, for a pixel in the prediction set, it can be as-signed a probability by using its discriminant score as xin the above calculation.

Probability image plots can be produced on the basisof Eqs. 10–13 to map a probability value to a correspond-ing color value. In this case, probability values were usedas x in the equations instead of discriminant scores, andsmax and smin were set to 1 and 0, respectively. The prob-ability level was used as the threshold value, t. Figure 6shows the resulting image plots for run 17 on day 2 forthe plume area at probability levels of 95 (top) and 80%(bottom). Similar probability-based images were obtainedfor run 2 on day 1. Table IV lists the number of CO2

pixels detected by the classi� er at different probabilitylevels for each data run. These results were calculated bycounting the number of pixels with probabilities greaterthan the speci� ed thresholds.

Inspection of Fig. 6 and Table IV reveals that, as ex-pected, more pixels are classi� ed as having the CO2 sig-nature as the probability threshold is relaxed. For exam-ple, at the 95% probability level, 6 primary reformer pix-els are detected. At the 90% probability level, 14 primary

reformer pixels and one urea � are pixel are detected. Thissuggests that the pixels near the urea � are stack have adifferent pattern than those near the primary reformer.This is con� rmed by inspection of the FT-IR spectra inFig. 2. Because the training set contained only pixelsfrom the area of the primary reformer, the classi� er isless sensitive to the more complex patterns observed inthe urea � are pixels. The CO2 signature in the region ofthe urea � are is detected, however, and the incidence offalse detections has been effectively removed by use ofthe probability-based classi� cation.

CONCLUSION

In this study, automated detection protocols have beenestablished for the detection of chemical plumes and CO2

from a remote platform based on multispectral imagingdata. This is the � rst step of a series of on-going studiesin our laboratory utilizing the RS-800 multispectral in-frared line scanner to implement automated chemical va-por detections. Excellent results were obtained for theplume/nonplume classi� er, and the performance of theprobability-based CO2 /non-CO2 classi� er is also quitepromising.

It is anticipated that the performance of the classi� erscan be further improved by the development of a better� lter design protocol. The � lters used here were selectedwithout a formalized optimization procedure. Throughthe application of numerical simulation and optimizationtechniques to � eld and laboratory spectral data, it is an-ticipated that greater chemical selectivity can be incor-porated into the � lter set. In future studies, the chemicalselectivity of the imaging data will be further evaluatedthrough work on the detection of ethanol and ammoniavapors generated through controlled release experi-ments.

ACKNOWLEDGMENTS

Funding for this research was provided by the Department of theArmy. Robert T. Kroutil, Roger J. Combs, and Robert B. Knapp of theU.S. Army Edgewood Chemical Biological Center are acknowledgedfor providing the ground FT-IR data displayed in Fig. 2. Dale Stagebergand Randy Zywicki of Raytheon TI Systems, Inc. are acknowledgedfor providing the calibrated and registered multispectral images used inthis research. Initial results from this work were presented in March,2000, at the Pittsburgh Conference and Exposition on Analytical Chem-istry and Applied Spectroscopy in New Orleans, LA. Portions of thisresearch were also presented in November, 2001, at Photonics Bostonin Newton, MA.

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