submitted papers

Remote Detection of Volatile Organic Compounds by PassiveMultispectral Infrared Imaging Measurements

MUKIRE J. WABOMBA,* YUSUF SULUB,� and GARY W. SMALL�Optical Science and Technology Center and Department of Chemistry, University of Iowa, Iowa City, Iowa 52242

Automated pattern recognition methodology is described for the detection

of signatures of volatile organic compounds from passive multispectral

infrared imaging data collected from an aircraft platform. Data are

acquired in an across-track scanning mode with a downward-looking line

scanner based on 8 to 16 spectral channels in the 8–14 and 3–5 lm

spectral ranges. Two controlled release experiments are performed in

which plumes of ethanol are generated and detected from aircraft

overflights at altitudes of 2200 to 2800 ft (671 to 853 m). In addition, a

methanol release from a chemical manufacturing facility is monitored.

Automated classifiers are developed by application of piecewise linear

discriminant analysis to the calibrated, registered, and preprocessed

radiance data acquired by the line scanner. Preprocessing steps evaluated

include contrast enhancement, temperature-emissivity separation, feature

selection, and feature extraction/noise reduction by the minimum noise

fraction (MNF) transform. Successful classifiers are developed for both

compounds and are tested with data not used in the classifier development.

Separation of temperature and emissivity by use of the alpha residual

calculation is found to reduce false positive detections to a negligible level,

and the MNF transform is shown to enhance detection sensitivity.

Index Headings: Infrared imaging; Remote sensing; Volatile organic

compounds; Minimum noise fraction; MNF; Pattern recognition.

INTRODUCTION

Remote infrared (IR) imaging techniques have come intoincreased use for the detection of chemical vapor species.1–8

These methods can be divided into two complementarycategories, active and passive, where active measurementsprobe the atmosphere with a laser source6–8 and passiveexperiments are based on measuring the naturally occurringradiance of a scene.2–5 Passive measurements have been morecommonly applied because of the limitations of laser sourceswith respect to available wavelengths, power, tunability, andreliability. Increasingly, however, sensor packages are beingdeployed that feature both active and passive capabilties.9

Passive measurements utilize the two principal atmosphericIR transmission windows, termed the mid-wave IR (MWIR, 3–5 lm) and long-wave IR (LWIR, 8–14 lm) regions. In cases inwhich reflected solar radiation can serve as the light source for

the passive sensor (e.g., when viewing a water backgroundfrom above4) or in which self-emission from hot gases ispresent, the MWIR region can be very useful. However, whenviewing gases at ambient temperature against terrain back-grounds that possess little reflectivity, the LWIR region is mostuseful because radiance from ambient temperature self-emission is dominant here. Under this scenario, chemicalsignatures are imparted on the IR background throughabsorption of the upwelling radiance at the characteristicvibrational frequencies corresponding to target analytes.

Two spectrometer designs are commonly used in passive IRimaging. Multispectral sensors employ a set of discrete, non-contiguous spectral channels, typically implemented withoptical filters.2,3,5 Hyperspectral instruments, based on eitherdispersive or multiplex designs, allow the scanning ofcontinuous spectra over a range of wavelengths.4,5,10 To obtainimages, the scene within the field-of-view (FOV) of the sensoris either sampled in discrete steps with a scanning optic (e.g., arotating mirror or prism) or a specialized optical system, andmultichannel detectors are used to map pixels in the scene ontoa two-dimensional grid of detector elements.

With both multispectral and hyperspectral instruments, twocommon implementations are used. High altitude measure-ments can be performed with the instrument mounted on eithersatellites3,5,10 or specialized aircraft.4,5 Measurements at lowaltitude employ conventional aircraft.2,4 The research presentedhere focuses on the use of a multispectral sensor operating atlow altitude (e.g., ,3000 ft or ;900 m) to perform detectionsof volatile organic compounds (VOCs) released from groundsources. Detection of chemical vapors is facilitated at lowaltitude because the upwelling ground radiance is much lessattenuated by the atmosphere than when the measurements aremade at high altitude through a long atmospheric path length.The presence of less atmospheric interference at low altitudealso serves to make multispectral sensors more capable (i.e.,the higher spectral resolution of hyperspectral sensors is lessimportant).

Because of the tremendous amount of data collected inmultispectral and hyperspectral imaging experiments, the dataanalysis methods used must be as automated as possible. Thekey to maximizing the retrieval of information is to takeadvantage of the multivariate nature of the data. Techniques forautomating the interpretation of multivariate images have beendeveloped in a variety of fields. Example applications includebiomedical imaging,11 chemical reaction or process monitor-

Received 2 October 2006; accepted 2 February 2007.* Present address: Millennium Pharmaceuticals, Inc., 40 Landsdowne St.,Cambridge, MA 02139.

� Present address: Novartis Pharmaceuticals Corp., One Health Plaza,Bldg. 401/B244B, East Hanover, NJ 07936-1080.

� Author to whom correspondence should be sent. E-mail: gary-small@uiowa.edu.

Volume 61, Number 4, 2007 APPLIED SPECTROSCOPY 3490003-7028/07/6104-0349$2.00/0

� 2007 Society for Applied Spectroscopy

ing,12,13 food analysis,14 and environmental remote sens-ing.2,15,16

In the research presented here, signal processing, imageprocessing, and pattern recognition methods are employed inthe development of an automated detection system for use withairborne multispectral IR images collected from ground sourcesof VOCs. The focus of the analysis is the reduction of scenepixels into a series of yes/no decisions regarding the presenceof a target analyte. Ethanol plumes from a controlled releaseexperiment and methanol plumes released from an industrialplant represent the detection targets in this work.

EXPERIMENTAL

Instrumentation. The images used in this work werecollected during three field experiments, termed experiments 1,2, and 3. All images were acquired with an RS-800SGmultispectral line scanner (Raytheon TI Systems, McKinney,TX) mounted in a downward-looking mode on board eitherDC-3 (experiment 1) or AeroCommander 500B aircraft(experiments 2 and 3). The imager implements an across-trackscanning procedure in which the ground is scanned along scanlines perpendicular to the flight line. This is illustrated in Fig. 1.The refractive scan element of the sensor is a double-dovegallium arsenide prism that is rotated at 30 Hz. This rotatingprism scans a 32-element cryogenically cooled photoconduc-tive Hg:Cd:Te (MCT) array detector across an unobstructed608 FOV. This FOV is divided into a set of discretely sampledpoints. For the data employed in this work, 1300 to 1400 pointswere sampled along each scan line.

Two calibrated internal greybody sources are locatedadjacent to each side of the 608 FOV and each provides 50pixels for use in calibrating each scan line. Directly below thescanner, the pixel size on the ground is approximately 1 m2 atan altitude of 2800 ft (853 m). As illustrated in Fig. 1, the total

number of pixels in a measured image can be expressed assamples 3 lines, where the number of samples denotes thepixels associated with each scan line and the number of linesspecifies the number of scan lines acquired along the flightpath.

As currently implemented, up to 16 of the 32 detectorelements have cooled bandpass optical filters bonded to them,producing a set of wavelength-selective detection channels. Forexperiments 1, 2, and 3, respectively, 14, 16, and 8 filters wereinstalled. Table I lists the band centers and widths for the filtersets associated with each of the three field experiments.

Procedures. The data collection consisted of flight passesby the aircraft across a target location on the ground. Datacollection with the imager was started and stopped as theaircraft passed over the target. Altitudes varied from 2200 ft(671 m) to 2800 ft (853 m). Nominal airspeeds were 90 knotsfor the DC-3 and 150 knots for the AeroCommander 500B.From run to run and day to day, exact altitudes and airspeedsvaried with meteorological conditions.

The data used in this work were collected at three differenttimes of the year and at three locations with very differentscenes. Experiment 1 was conducted in winter and spannedfour consecutive days and 120 flight passes over a controlledrelease stack.17 Table II lists the run numbers for the imagesused in this work. The scene background was entirely groundterrain. The stack was placed at the center of a mowed circlewith a radius of 150 ft. Other equipment within the perimeterincluded a trailer, several ground-based spectrometers, ameteorological station, a windsock, and a calibration panel.Three other calibration panels were placed outside thisperimeter. Six compounds were released: ethanol, ammonia,sulfur hexafluoride, methanol, sulfur dioxide, and hydrogenchloride. The stack temperatures ranged between 173 to 2088C, and path-averaged concentrations were 1712 to 7545 ppm-m for the ethanol data. Data acquired during the ethanolreleases were the primary focus of the work reported here.

Experiment 2 was conducted in mid-summer on a single day.Releases of ethanol were made with a modified version of thecontrolled release stack. The stack diameter was increased from

FIG. 1. Schematic of the data collection. The scanning prism allows theinterrogation of individual scene pixels at discrete sampling positions along a608 arc that is perpendicular to the flight track. The samples and lines thatdefine the pixels of the image are indicated in the figure. For a given samplingpoint at line i and sample j, a different ray trace exists between the ground andeach of the detector elements. This factor, coupled with the motion of theaircraft, causes each spectral channel to view a slightly different groundposition and motivates the need for registration of the image before the data canbe interpreted.

TABLE I. Filter sets used for field experiments.

Channel

Experiment 1 Experiment 2 Experiment 3

Centera

(lm)Widthb

(lm)Centera

(lm)Widthb

(lm)Centera

(lm)Widthb

(lm)

1 10.91 6.21 9.67 0.71 9.67 0.712 9.40 1.06 10.38 0.19 10.38 0.193 10.65 0.41 8.70 0.77 8.70 0.774 8.30 1.18 10.73 0.18 10.73 0.185 8.80 0.84 11.24 0.89 11.24 0.896 9.50 0.73 10.58 0.37 10.58 0.377 11.30 0.97 9.45 0.75 9.45 0.758 10.50 1.27 9.29 1.02 9.29 1.029 3.91 2.60 4.40 0.09510 3.41 0.08 4.31 0.09111 4.22 0.11 4.37 2.6712 3.92 0.09 4.35 2.6813 4.16 1.49 3.89 0.09414 5.35 0.35 5.28 0.3415 4.22 2.3516 3.45 0.08

a Denotes the center position of the filter passband.b Band widths are expressed as the full-width at half-height of the filter

passband.

350 Volume 61, Number 4, 2007

0.4 to 1 m to allow a larger gas plume to be generated. Thestack was placed adjacent to an airport facility. The sceneincluded both natural terrain and a variety of buildingstructures and materials. Table II lists the images used in thisresearch.

Experiment 3 was performed in late spring at a chemicalmanufacturing facility that produced a variety of chemicalspecies such as methanol, ammonia, formaldehyde, penta-erythritol, plasticizers, synthetic lubricants, nitric acid, andammonium nitrate. The basic feeds to this facility weremethanol, acetaldehyde, sodium hydroxide, and valeric acid.The scene was extremely complex with water, natural terrain,and a large number of buildings and other structures such asmetal pipes. Data were acquired over four days, and 146images were collected. Table II lists the images used here. Datacollected on day 4 during plant start-up, which resulted in amethanol release, is the focus of the work described here.

The collected images were transferred to Silicon GraphicsIndigo2 IMPACT 10000 workstations (Silicon Graphics,Mountain View, CA) where the computations reported herewere performed. These computers operated under Irix (Version6.5, Silicon Graphics, Inc.). Preprocessing of the images wasperformed with both original and built-in code implementedwith the ENVI software package (Version 3.4, ResearchSystems, Boulder, Co). Calibration and registration of theimages was performed with ENVI code provided by RaytheonTI Systems, Inc. All other calculations described here wereperformed with Matlab (Version 6.1, The MathWorks, Inc,Natick, MA).

RESULTS AND DISCUSSION

Overview of Research Objectives. The goal of thisresearch was to develop an automated procedure for generatinganalyte-specific classification images from the multispectralimages acquired by the line scanner. A classification image isan image in which the intensity of each pixel indicates thestrength of the analyte signature within that pixel. This allowschemical targets within the scene to be identified for furtherinvestigation. The steps involved in computing the classifica-tion image are: (1) calibration and registration of the raw imageto correct for changes in sensor response and geometricalartifacts, (2) preprocessing the image to extract relevantinformation for the analyte detection (i.e., suppression ofbackground variation while extracting selective informationabout the analyte), and (3) use of numerical pattern recognitionmethodology to implement the classification of each pixel.

Experiments 1 and 2 were conducted for the purpose of

developing an algorithm for detecting ethanol. Initial work onthe ethanol detection was performed with data from experiment1. These images consisted of a small ethanol plume and arelatively simple terrain background. Experiment 2 wasperformed with the modified stack that allowed larger ethanolplumes to be generated, although the scene background in thiscase exhibited increased complexity. The data from experiment2 provided more ethanol-containing pixels for use in trainingthe classifier. Experiment 3 focused on the detection ofmethanol at an industrial site. In this case, the plume wasextremely large and the background was very complex.

Image Calibration and Registration. The digital dataacquired from the imager was provided in arbitrary (i.e.,uncalibrated) units. In order to make the numerical responsescompatible from run to run and day to day, the raw data werefirst converted into radiance units.18,19 For each scan line, thedigital responses of the two internal greybody sources wereused to establish a linear calibration between detector responseand radiance. The greybody sources were set to temperaturesthat bracketed the expected ground radiances, typically 25 and45 8C. These internal sources were calibrated regularly bymeasurements at multiple temperatures with an external NIST-traceable blackbody. The details of this calibration procedurehave been reported previously.20

As can be visualized in Fig. 1, a complication resulting fromthe use of an array detector is that a different ray trace existsbetween each detector element and the ground scene. Thus,each element in the array sees a different spot at the scene at agiven moment in time. This causes mis-registration in thespectral channels. In addition, use of the rotating prism to scanacross the track causes a change in perspective across the scanline. Therefore, co-registering the channels in such a way that apixel in one channel has the same information as that pixel inthe rest of the channels is important. This preprocessing stepwarps and aligns the separate spectral channels so that a givenpoint in the image data addresses the same point on the ground.These operations were used to align the pixels properly withina scan line. Between scan lines, gyroscope and globalpositioning systems on-board the aircraft were used to correctfor geometrical distortions such as yaw, pitch, roll, and jitter.The calibration and registration operations described abovewere performed on each image before any further calculationswere attempted.

Image Preprocessing. Several preprocessing strategieswere evaluated for potential use in enhancing the analyteinformation within the data. These fall into the categories ofmethods to enhance contrast between the plume and non-plumepixels, techniques for removing the effects of temperature

TABLE II. Summary of images from field experiments.

Experiment 1 Experiment 2 Experiment 3

Run Description Usea Run Description Usea Run Description Usea

35 Ethanol release T 2 Ethanol release T 20 Methanol release T26 Ethanol release T 3 Ethanol release P 26 Methanol release P27 Ethanol release T 1 Ammonia release P29 Ethanol release T 15 Carbon dioxide release P32 Ethanol release T34 Ethanol release T24 Ethanol release P28 Ethanol release P

a Indicates use of image for training the discriminant (T) or in a prediction test of the computed discriminant (P).

APPLIED SPECTROSCOPY 351

variation from the data, and feature reduction/extractionstrategies.

Contrast Enhancement. Mean centering was used toremove common features from the images and thereby enhancecontrast. This calculation was implemented by computing themean radiance in each band across the image and thensubtracting that value from the corresponding band in eachpixel. To eliminate the problem of negative values aftersubtracting the mean, the radiances were translated to aminimum of zero by subtracting the minimum radiance in theimage.

Spatial convolution filtering was also investigated for use inenhancing the images. This filtering step replaced a pixel bij atlocation i, j in the input image with some weighted average ofbrightness values of its neighbors. A 3 3 3 low passconvolution filter whose central coefficient was two times theperipheral coefficients was used to de-emphasize high-frequency detail from the region of interest. This filter reducedblurring at the edges that resulted from averaging. The primaryinput pixel was always located at the center of the 3 3 3 grid.The operation was then shifted to the next pixel and this wasrepeated for every pixel in the image.21,22 This processing stepwas found to be beneficial in enhancing contrast between theplume and non-plume pixels in the images from experiment 1but was not helpful when applied to the data from experiments2 and 3, which had more complex backgrounds.

Temperature-Emissivity Separation. The effect of tem-perature on the acquired data can be understood by inspectionof Planck’s function:

Lj ¼ejC1

k5j p e

C2kj T � 1

� � ð1Þ

In Eq. 1, Lj is the spectral radiance (W m�3 sr�1) in channel jwhen the sensor is viewing a material with an emissivity of ej

and a surface temperature of T (K), kj is the center wavelengthof channel j (m), C1 is termed the first radiation constant (1.1913 10�16 W m2 sr�1), and C2 is the second radiation constant(1.439 3 10�2 m K). Emissivity varies over the range of 0 to 1and is a measure of how efficiently a material radiates energycompared to a theoretical blackbody. Emissivity is thus definedas

ej ¼ Lj=Lj; blackbody ð2Þ

where Lj,blackbody is the spectral radiance of a true blackbody atthe same temperature. As indicated in Eqs. 1 and 2, emissivityis a function of wavelength and is characteristic of the material.For this reason, the emissivity is the key component of theradiance that provides information for discriminating onematerial from another.

Inspection of Eq. 1 reveals that emissivity and temperatureboth contribute to the radiance observed in channel j.Temperature thus represents a significant interference in theextraction of chemical information from the measured radiance.For this reason, significant effort has been devoted to thedevelopment of methods to remove the effect of temperaturefrom Eq. 1.23–25

In previous work, we have applied one of these algorithms,termed the alpha residual technique, to the problem ofseparating temperature and emissivity in multispectral IRimaging data.2 This approach is based on a transformation of

Eq. 1 to remove the temperature term. This is accomplished byuse of Wien’s approximation of Planck’s function in which the‘‘�1’’ term in the denominator of Eq. 1 is discarded. For atemperature of 300 K and a wavelength of 10 lm, Wien’sapproximation results in errors in the radiance value of lessthan 1%. The natural logarithm is then taken of both sides ofthe equation, followed by multiplication by kj to separate the kand T terms. The mean of the equation set is then calculatedover the N spectral channels of the sensor, and the difference(residual) is taken between each term for channel j and thecorresponding mean term computed over all the channels. Thetemperature effect is removed from these difference expres-sions because the temperature contributes equally to eachchannel and thus is subtracted.

The resulting transformed radiance is termed the alpharesidual, aj:

aj ¼ kj ln Lj �1

N

XN

j¼1

kj ln Lj þ Kj ð3Þ

In Eq. 3, Kj is a constant computed from the wavelengthcenters of the channels and the first radiation constant (C1). Thevalue of aj is proportional to the difference between thelogarithmized emissivity of channel j and the mean logarith-mized emissivity of the N spectral channels. Through thiscalculation, the contribution of temperature variation to themeasured radiance values is minimized. In this research, whenthe alpha residual method was used, the aj values werecomputed after application of the contrast enhancementmethods described above.

Feature Reduction and Extraction. The measured data ineach spectral channel consisted of a mixture of relevant andirrelevant information with respect to the detection of the targetanalyte. Feature reduction and extraction strategies attempt toenrich the data in analyte information by discarding orsuppressing irrelevant information or noise. Two approacheswere investigated in this work.

The simplest feature reduction procedure is the eliminationof spectral channels from the pattern recognition step. Thisstrategy is based on the identification of spectral channels thatcontain little useful information regarding the desired discrim-ination (e.g., identification of analyte versus non-analytepixels). In this work, red-green-blue (RGB) images wereconstructed from groups of three channels and visuallyinspected to identify channels that appeared to contribute littleinformation for use in discriminating the structures at the site orthat contained largely noise. Once identified, these channelswere removed from further work.

For the data from experiment 3, a feature extraction methodwas investigated based on transforming the computed alpharesiduals by use of the minimum noise fraction (MNF)transform.26 This is a latent variable method in which datafrom the N spectral channels are analyzed to computeunderlying components or sources of variation. The MNFmethod is conceptually similar to principal component analysis(PCA)27 and is used to determine the inherent dimensionalityin the data and also to segregate noise from the data. The PCAtechnique has been widely used in multivariate imageanalysis.12,14,15

The MNF calculation is a two-step process. The firsttransformation is based on the estimated noise covariancematrix. This step decorrelates and rescales the noise in the data.

352 Volume 61, Number 4, 2007

The second step is a standard PCA performed on the noise-whitened data. The dimensionality is determined by inspectingthe eigenvalues and the associated images. The elimination ofcomponents associated with small eigenvalues helps tosuppress noise in the data.27

Selection of Training Data for Classifier Development.For the data from each experiment, images were placed intotraining and prediction categories. Training images were usedto train the automated classifiers, while the prediction imageswere used to test the classifiers after training. For the trainingimages, individual pixels had to be divided into analyte-activeand analyte-inactive categories for use with the supervisedpattern recognition methods employed in this work. This taskwas accomplished by first grouping the pixels with similarsignatures together.

The unsupervised K-means clustering algorithm28 was usedto assign each pixel to one of K data clusters. The algorithmemploys an iterative procedure to assign patterns to separateclusters without any prior knowledge by minimizing thesquared distances of the patterns to their respective clustermeans. The algorithm was run five times with different randomstarting cluster assignments and the user-specified number ofclusters was varied from 3 to 10. Stable clusters were formedwhen this number was 7.

After determining the cluster assignments, each pixel wasassigned an integer index corresponding to its cluster number.The membership integers were reconstructed into a matrix ofthe size of the image matrix, and a color image was generatedby associating each index with a distinct color. Theseclustering images were referenced to scene photos, maps,etc., to determine what each represented. By knowledge of thesite, clusters likely corresponding to chemical plumes wereelucidated. This allowed the assignment of pixels to analyte-active and analyte-inactive categories.

Pattern Recognition Analysis. Piecewise linear discrimi-nant analysis29,30 was used to generate classifiers forautomatically assigning pixels to analyte-active and analyte-inactive categories. Results were converted into discriminantscore images for presentation. The discriminant score for eachpixel was calculated, followed by mapping negative scores(analyte-inactive) to a scale of 0–18.5 and positive scores(analyte-active) to 44.5–63. When mapped onto a standardcolor map, this produced color ranges of dark to light blue forthe analyte-inactive class and yellow to red for the analyte-active class. By using these values to reconstruct the imagematrix, images were created in which pixels with colors ofyellow to red signaled the presence of the analyte.

Analysis of Data from Experiment 1. Five channels withfilter centers at 9.40, 10.65, 8.30, 9.50, and 11.30 lm wereused in the analysis (channels 2, 3, 4, 6, and 7 in Table I).Channel 6 (9.50 lm) was the on-band channel for the analyte,ethanol. As described above, channel selection was based onvisual assessment of RGB images formed from combinationsof three channels.

The clustering procedure described above was applied to theimages from experiment 1. Clustering was applied to bothradiance and alpha residual data. The number of clusters wasset to 7. In both cases, mean centering and filtering were usedas described previously to provide contrast enhancement. Someimages did not produce clusters that could be identified ascontaining just plume pixels. For this reason, a two-stepprocedure was used to form the training set.

Run 35 had a clearly defined plume cluster. An initialdiscriminant was generated by use of a training set formedfrom run 35 pixels only. This initial training set had 13 analyte-active and 987 randomly selected analyte-inactive pixels forradiance data and 11 and 989 pixels for the alpha residual data.The computed piecewise linear discriminant was based on onediscriminant function and separated 100% of the analyte-activepixels. The computed discriminant was then used for predictionon runs 26, 27, 29, 32, and 34, and pixels that were predicted asanalyte-active were accumulated to form a new training setwith 58 analyte-active pixels. These pixels were augmentedwith 942 randomly selected analyte-inactive pixels from run 35to form a training set with 1000 pixels.

With this training set, new discriminants were computed(one discriminant function) with both radiance and alpharesidual data. Classification percentages were 93.1 and 89.6%.The final discriminants were then used for prediction on runs24 and 28 that were not used in the selection of pixels to beused during the training stage. The results showed that theclassifier generated with the radiance data produced asignificant number of false detections when applied to theprediction images. Improved performance was observed withthe discriminant based on the alpha residuals. The plume pixelswere clearly identified in the discriminant score image and thenumber of false detections was negligible.

Analysis of Data from Experiment 2. The original datafiles were resized from approximately 1500 samples 3 1250lines to 100 samples 3 80 lines. There were four runs but onlytwo were used in this work because the stack was missed in run4 and was not identifiable in run 1. Run 2 was used to select thetraining set, while run 3 was used as the prediction set.

Figure 2A shows an RGB IR image of the site based onchannels with center wavelengths of 9.29, 9.45, and 9.67 lm.The white trail in the upper middle of the image corresponds tothe ethanol plume. In this experiment, 10 channels were usedand were selected as before on the basis of visual inspections ofRGB images. These included channels 3, 5, 6, 9, 10, 11, 12, 13,15, and 16 in Table I.

The clustering procedure was applied to run 2 with thenumber of clusters set to 7. In the clustering results based onboth radiance and alpha residual data, the plume pixels wereobserved in two clusters. This is because the plume cools as itdisperses and the analyte concentration decreases. Forpresentation, only the clustering results based on the alpharesidual data are shown since the corresponding results basedon the radiance data were similar. Figure 2B shows the alpharesidual clustering results with bright red and yellowcorresponding to ethanol in run 2.

There were fewer pixels in the plume clusters when the alpharesidual data were used. There were 24 and 16 active pixels inthe clustering results based on the radiance and alpha residualdata, respectively. In both cases, the training data set wascomposed of 1000 pixels, the analyte-active pixels, andrandomly selected analyte-inactive pixels from the trainingrun. This procedure resulted in different training sets for theradiance and alpha residual data. The discriminant in each caseseparated all the analyte-active pixels. These discriminantswere then used for prediction on the training data set and theindependent prediction set for both the radiance and alpharesidual data.

Figure 2C is the discriminant score image for run 2 based onradiance data. Figures 2D and 2E are the discriminant score

APPLIED SPECTROSCOPY 353

FIG. 2. (A) RGB image of the data collection site for experiment 2 based on channels with filter positions at 9.29, 9.45, and 9.67 lm. The white trail in the uppermiddle of the image represents the pixels corresponding to the ethanol plume. (B) Clustering image for run 2 based on alpha residual data with the bright red andyellow clusters indicating the ethanol pixels. (C) Discriminant score image for the radiance data for run 2 obtained by using the discriminant developed from theradiance data of the same run. (D) Discriminant score image for alpha residual data for run 2. (E) Discriminant score image for alpha residual data for run 3. Run 3was not used in training the discriminants.

354 Volume 61, Number 4, 2007

images for runs 2 and 3, respectively, when the alpha residualdata were used. Run 3 was an independent prediction runwhose pixels were not used in the training stage. Comparingthe results in Figs. 2C and 2D, the discriminant score imagebased on the radiance data shows a tail with more yellow pixelson the outer side. This shows the variation in temperature. Theyellow pixels show the cooler region of the plume, while thehotter region is displayed as red. The discriminant score imagesfor the alpha residual data had one misclassification, as shownon the right hand corner, and a smaller plume region. Thissuggests a possible decrease in analytical sensitivity as a resultof the alpha residual calculation.

Analysis of Data from Experiment 3. The results of fourruns, two of which contained methanol, one ammonia, and theother hot carbon dioxide, were investigated. Run 20 was usedto select the training set for use in building the methanolclassifier. When just the alpha residual data were used,clustering was based on channels 1, 3, 7, and 8 in Table I.When the MNF transform was applied to the alpha residualdata, channels 1–8 were used in the calculation of the MNFfactors.

Figures 3A, 3B, 3C, and 3D display grayscale images based

on the projection of each pixel in a cropped region of run 20onto MNF components 1 to 4, respectively. This regioncontains the source of the methanol plume, corresponding tothe dark trail in component 2. Also, structures at the industrialplant are clearly visible in the first three components withincreasing degradation. The fourth component contributeslargely noise and hence only three MNF components wereused in the clustering and piecewise linear discriminantcalculations. The first three MNF factors accounted for83.65% of the variance in the data.

Figure 4A shows an IR RGB image constructed from all thepixels in run 20. The channels used were located at 9.29, 9.45,and 9.67 lm, corresponding to channels 8, 7, and 1,respectively, in Table I. Channel 1 was the on-band methanolfilter. Figure 4B is an MNF-processed RGB image based on thefirst three factors. The methanol plume is clearly more visiblein this figure. This is explained by the fact that the MNFcalculation concentrates information in the three factors used toform the RGB image. The size of panels A and B was 1447samples 3 1691 lines.

Clustering was again used to select the analyte-active andnon-analyte patterns comprising the training data sets. The

FIG. 3. Grayscale images for each of the first four MNF components computed from alpha residual data of run 20 in experiment 3. Components (A) 1, (B) 2, (C) 3,and (D) 4. The images show degradation of information in the later components. Only three factors were used in the data analysis.

APPLIED SPECTROSCOPY 355

number of clusters was set to 7, and clustering was applied tothe same cropped region of run 20 displayed in Fig. 3. Clearmethanol clusters were defined with both types of input data.There were 2990 and 3873 analyte-active pixels when theclustering procedure was applied to the alpha residual data andthe MNF-transformed alpha residual data, respectively. It isapparent that the MNF transform facilitated the identification ofthe methanol pixels.

To form the training sets in each case, the analyte-activepixels were supplemented with randomly picked analyte-inactive pixels from the cropped region in run 20 to produce atotal of 10 000 patterns. The piecewise linear discriminantswere based on two discriminant functions in each case. For thealpha residual data, the discriminant correctly separated76.96% of the analyte-active pixels, with the first discriminantfunction classifying 76.22% of the pixels. For the MNF-

FIG. 4. (A) Image in RGB format constructed from channels located at 9.29, 9.45, and 9.67 lm for run 20 in experiment 3. The source of the methanol plume isindicated by the circle. (B) Image in RGB format based on the first three MNF components computed from the alpha residual data of run 20. The source of themethanol plume is indicated by the circle.

FIG. 5. (A) Discriminant score image for the complete image of run 20 in experiment 3 when alpha residual data were used. (B) Corresponding discriminant scoreimage when the MNF-transformed alpha residual data were used. The discriminant based on the MNF-processed data exhibits greater ability to detect methanolpixels in the outer plume region

356 Volume 61, Number 4, 2007

transformed alpha residual data, the discriminant separated95.09% of the analyte-active pixels, with the first discriminantfunction separating 93.93% of the pixels.

Figures 5A and 5B are the discriminant score images for thealpha residual and MNF-transformed alpha residual data,respectively, for the full (i.e., uncropped) image of run 20.The key issue in evaluating these plots is the performance ofthe discriminant outside of the cropped region that correspond-ed to the training set. A comparison of Figs. 5A and 5B revealsthat the discriminant based on MNF processing providedsignificantly better performance in identifying methanol pixelsin the less dense, outer plume region. There are negligible falsepositives in either image when they are compared to Figs. 4Aand 4B.

The discriminant based on MNF-transformed alpha residualdata was next applied to runs 26, 1, and 15. None of the pixelsin these runs was used in training the discriminant. Run 26(1259 samples 3 692 lines) was acquired during the time of thesame methanol release (day 4) that was captured in run 20.Figure 6A displays the discriminant score plot obtained. Theprediction results of this image show the presence of thepreviously observed methanol pixels in the clustering results,aligned in the direction of the wind. Only a scattering of pixelsappear to be false positives.

The image sizes for runs 1 and 15 were 1113 samples 31272 lines and 1433 samples 3 507 lines, respectively. Runs 1and 15 were acquired on days 1 and 3 of experiment 3,respectively. These images were recorded during overflights ofthe facility when it was known that no methanol was beingreleased. Furthermore, it is known that plumes of ammonia andhot carbon dioxide were present during runs 1 and 15,respectively. Figure 6B displays the discriminant score plotcorresponding to run 1. A few scattered false positives arenoted in the image, but there is no evident plume. Thecorresponding discriminant score plot for run 15 was verysimilar in character to that from run 1. No plume was evident,and only a small number of scattered false positives wereobserved.

These results from experiment 3 suggest that there issignificant chemical selectivity in this analysis method, evenwith a limited number of spectral channels. Furthermore, the

successful application of the discriminant trained with datafrom day 4 to data collected on different days is significant.

CONCLUSION

The data analysis techniques described here are compatiblewith real-time implementation with an airborne sensor for usein remote sensing chemical imaging applications. Thismethodology provides the capability for automated detectionof a target analyte and for locating the source of the species ofinterest. Once the classifiers have been trained, they can beimplemented with no requirement for user intervention ormanual data interpretation. This work represents the firstautomated image analysis system for the selective detection ofspecific VOCs from passive multispectral IR images.

The results presented in this work also show that the analysistechnique is capable of identifying and locating plumes, evenwhen only a small number of analyte-active pixels are availablefor use in the training data set. This was demonstrated by theresults of experiments 1 and 2 in which the number of pixels ofethanol used for the development of the classifier was limited.In ongoing research, we are further exploring this issue of howbest to train classifiers under conditions in which the amount ofanalyte-active data is limited. Of particular interest in thisregard are techniques for simulating analyte-active data fromlaboratory measurements.

In terms of chemical selectivity, the results of experiment 3were especially significant. Successful detection of themethanol plume was achieved, and when applied to the runscontaining hot carbon dioxide and ammonia, the methanolclassifier rejected these compounds with very few falsedetections. Although scattered pixels were judged to be falsepositives, these occurrences may be caused by incompleteremoval of temperature effects by the alpha residual calcula-tion.

Finally, the instrumental hardware and aircraft platformemployed in this research are relatively unsophisticated whencompared to many of the hyperspectral imaging systemsdeployed on satellites or high altitude aircraft. The excellentdetection performance and good analyte selectivity achieved inthis research were keyed by the use of low-altitude measure-

FIG. 6. Discriminant score images for runs (A) 26 and (B) 1 from experiment 3 based on the use of MNF-transformed alpha residual data. Run 26 was collectedduring the same methanol release captured in run 20. Run 1 was collected on a different day and is known to contain a plume of ammonia but no methanol.

APPLIED SPECTROSCOPY 357

ments. By minimizing atmospheric interference, successfuldetections of VOCs could be made without the need for high-resolution hyperspectral data.

ACKNOWLEDGMENTS

Funding for this research was provided by the Department of the Army. DaleStageberg and Randy Zywicki of Raytheon TI Systems, Inc. are acknowledgedfor providing the multispectral images used in this research and the calibrationand registration software. Robert Kroutil of Los Alamos National Laboratoryand Mark Thomas of the U.S. Environmental Protection Agency are thankedfor their contributions to this work.

1. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: AnIntroduction (Springer, New York, 1999), 3rd ed.

2. L. Zhang and G. W. Small, Appl. Spectrosc. 56, 1082 (2002).3. V. J. Realmuto and H. M. Worden, J. Geophys. Res. 105, 21497 (2000).4. I. Leifer, D. Roberts, J. Margolis, and F. Kinnaman, Earth Planet. Sci. Lett.

245, 509 (2006).5. M. J. Hellman and M. S. Ramsey, J. Volcan. Geotherm. Res. 135, 195

(2004).6. A. Lavrov, A. B. Utkin, R. Vilar, and A. Fernandes, Appl. Phys. B 76, 87

(2003).7. T. Gasmi, H. Zeaiter, G. Ropero, and A. Gonzalez Urena, J. Opt. A: Pure

Appl. Opt. 2, 565 (2000).8. B. Presennakumar, A. Kumar S., D. R. Rao, S. M. Nair, M. S.

Vasudevanpillai, S. Veerabuthiran, and M. Satyanarayana, Proc. SPIE-Int. Soc. Opt. Eng. 6409, 64090X/1 (2006).

9. D. M. Winker, W. H. Hunt, and C. A. Hostetler, Proc. SPIE-Int. Soc. Opt.Eng. 5575, 8 (2004).

10. K. Bogumil, J. Orphal, T. Homann, S. Voigt, P. Spietz, O. C. Fleischmann,A. Vogel, M. Hartmann, H. Kromminga, H. Bovensmann, J. Frerick, and J.P. Burrows, J. Photochem. Photobiol., A 157, 167 (2003).

11. T. W. Nattkemper, J. Biomed. Inform. 37, 380 (2004).

12. R. J. Hendershot, R. Vijay, B. J. Feist, C. M. Snively, and J. Lauterbach,Meas. Sci. Technol. 16, 302 (2005).

13. S. P. Gurden, E. M. Lage, C. G. de Faria, I. Joekes, and M. M. C. Ferreira,J. Chemom. 17, 400 (2003).

14. A. Antonelli, M. Cocchi, P. Fava, G. Foca, G. C. Franchini, D. Manzini,and A. Ulrici, Anal. Chim. Acta 515, 3 (2004).

15. G. P. Ellrod, J. Volcan. Geotherm. Res. 135, 91 (2004).16. J. A. Griffith, Water, Air, Soil Pollut. 138, 181 (2002).

17. C. T. Chaffin, Jr., T. L. Marshall, and N. C. Chaffin, Field Anal. Chem.Technol. 3, 111 (1999).

18. R. A. Schowengerdt, Techniques for Image Processing and Classificationin Remote Sensing (Harcourt Brace Jovanovich, New York, 1983).

19. W. F. Screiber, Fundamentals of Electronic Imaging Systems: SomeAspects of Image Processing (Springer-Verlag, New York, 1986).

20. R. W. Zywicki, Proc. SPIE-Int. Soc. Opt. Eng. 3537, 237 (1999).

21. F. Van der Meer and S. M. DeJong, Int. J. Remote Sens. 21, 2781 (2000).22. I. Pitas, Digital Image Processing Algorithms and Applications (John

Wiley and Sons, New York, 2000).23. S. J. Hook, A. R. Gabell, A. A. Green, and P. S. Kealy, Remote Sens.

Environ. 42, 123 (1992).24. Z. L. Li, F. Becker, M. P. Stoll, and Z. M. Wan, Remote Sens. Environ. 69,

197 (1999).

25. P. S. Kealy and J. H. Simon, IEEE Trans. Geosci. Remote Sens. 31, 1155(1993).

26. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, IEEE Trans.Geosci. Remote Sens. 26, 65 (1988).

27. E. R. Malinowski, Factor Analysis in Chemistry (John Wiley and Sons,New York, 1991), 2nd ed.

28. W. L. Martinez and A. R. Martinez, Computational Statistics Handbookwith MATLAB (Chapman and Hall/CRC, New York, 2002).

29. T. F. Kaltenbach and G. W. Small, Anal. Chem. 63, 936 (1991).

30. R. E. Shaffer and G. W. Small, Chemom. Intell. Lab. Syst. 32, 95 (1996).

358 Volume 61, Number 4, 2007