Multi-channel tunable imager architecture for ...

Multi-channel tunable imager architecture for hyperspectral imaging in relevant spectral domains CHHAVI GOENKA,1,2* JOSHUA SEMETER,1 JOHN NOTO,2 JEFFREY BAUMGARDNER,1 JUANITA RICCOBONO,2 MIKE MIGLIOZZI,2 HANNA DAHLGREN,3 ROBERT MARSHALL,4 SUDHA KAPALI,2 MICHAEL HIRSCH,1 DONALD HAMPTON,5 HASSANALI AKBARI1 1

Boston University, 8 St. Mary’s Street, Boston, MA 02215, USA Scientific Solutions Inc. (SSI), 55 Middlesex Street, Unit 210, North Chelmsford, MA 01863, USA 3 KTH Royal Institute of Technology, Stockholm, Sweden 4 Stanford University, 350, Serra Mall, Stanford, CA 94305, USA 5 Geophysical Institute, University of Alaska, Fairbanks, AK 99775, USA *Corresponding author: [email protected] 2

Received XX Month XXXX; revised XX Month, XXXX; accepted XX Month XXXX; posted XX Month XXXX (Doc. ID XXXXX); published XX Month XXXX

In this paper, we present a technique for dimensionality reduction in hyperspectral imaging during the data collection process. A four channel hyperspectral imager using Liquid Crystal Fabry-Perot (LCFP) etalons has been built and used to verify this method for four applications – auroral imaging, plant study, landscape classification and anomaly detection. This imager is capable of making measurements simultaneously in four wavelength ranges while being tunable within those ranges, and thus can be used to measure narrow contiguous bands in four spectral domains. In this paper, we describe the design, concept of operation and deployment of this instrument. The results from preliminary testing of this instrument are discussed and are promising and demonstrate this instrument as a good candidate for hyperspectral imaging. OCIS codes: (110.4234) Multispectral and hyperspectral imaging; (120.4570) Optical Design of Instruments; (120.2230) Fabry-Perot; (160.3710) Liquid Crystals; (300.0300) Spectroscopy; (150.1488) Calibration.

1. INTRODUCTION Hyperspectral imaging is the collection, processing and analysis of spectral data in numerous contiguous wavelength bands while simultaneously providing spatial context. The dataset obtained with hyperspectral imaging is a datacube with two dimensional spatial images with spectral information for each pixel. It has increasingly been used for remote sensing over the past decades for many applications such as environmental analysis [1]-[2], anomaly detection [3][4] and landscape studies [5][6]. It is also used in biomedical imaging[7]-[8] and industrial applications [9]. Some of the commonly used instruments for hyperspectral imaging are pushbroom scanning imaging systems [10]-[11] , grating-based imaging spectrometers [12] and more recently electronically tunable filters [13]. Electronically tunable filters offer the advantages of compactness and absence of

mechanically movable parts. They involve the use of materials whose response to light can be altered in the presence of an external stimulus. Liquid Crystal Tunable Filters (LCTF) [2] and Liquid Crystal Fabry-Perot (LCFP) [14] interferometers use liquid crystals which change their refractive index in the presence of an external electric field, and Acousto-Optic Tunable Filters (AOTF) use piezoelectric transducers bonded with a birefringent crystal [15]. One of the major challenges with hyperspectral imaging techniques is the amount of data generated. Often a large percentage of the data is redundant and makes the processing and analysis computationally expensive. Many band selection techniques have been developed to reduce the size and complexity of hyperspectral datasets[16-21]. These can use techniques such as Principal Component Analysis (PCA), Information Gain (IG) [22], and Virtual Dimensionality (VD) [23], among others. Such band selection methods work on the basic premise that not all spectral bands contain information

reelevant to the application. They significantly reduce th he am mount of data to be analyzed d but are still computationallly heeavy since they work towards band selection n during the posstprrocessing pha ase after the e data has been collecteed. Ad dditionally, the e time required for data colllection over th he en ntire spectrum is i long. While th his is acceptablee for applications wh here the scen ne is not chan nging rapidly, it could prov ve disadvantageous in time-sensitive applications such as daata co ollection from aerial a vehicles, civilian c search and rescue, or in vivvo medical im maging. Data redundancy and computation nal co omplexity both can be mitiga ated if the speectral bands are a seelected before th he collection off data, at least in n the cases wheere information abou ut the targets is known. So ome imagers ha ave recently bee en developed which w are capab ble off capturing specctral data in many wavelength hs at a time. Th he ad dvantage offerred by imagiing in multip ple channels is sim multaneity of the measurem ments which is important for f dyynamic events. The four-dimensional imagiing spectrometter (4 4D-IS) [24], the instantaneous hyperspectral camera [25] an nd th he Verde hyperrspectral camera [26] are so ome examples of su uch imaging sy ystems. These systems s have the capability of caapturing the da atacube in a single s exposure but still don n’t exxploit the red dundancy of the data sin nce they mak ke measurements in n hundreds of sp pectral bands. Th his research work w aims to add to the existing e array of sim multaneous hyperspectral ima agers and pressents a techniqu ue to collect hyperrspectral data in four pre-sselected spectrral do omains consistiing of contiguou us narrow band ds, thus reducin ng th he dimensionaliity of the data. The concept iss the same as th he afforementioned band selection n techniques bu ut here the ban nd seelection is done e before rather than after dataa collection, thus reeducing the amo ount of data to be collected an nd processed. Th he caapability of the instrument to capture four channeels sim multaneously reduces acquisition times significantly. In ad ddition to that, the t simultaneitty of captured im mages providess a temporal correla ation which is important i in so ome applications su uch as study ying the auro oral emissions [27], opticcal mammography [28], [ etc. The instrument is called c the Liqu uid ectral Imager (LiCHI) ( and co ombines the higgh Crrystal Hyperspe sp pectral resolutio on of Fabry-Perrot etalons with h the wavelenggth tu unability of the liquid crystals for the spectraal measurements. Fo our wavelength h bands are sho own to be sufficcient for spectrral an nalysis in a varriety of applica ations. Some exxamples of these include detection n of specific types of foreign materials m in cotto on [2 29], separating peeled p shrimpss from shells [12 2], distinguishin ng om lesions [28 8] and other similar problem ms. brreast tissue fro Th hus LiCHI can address a brroad range of problems whiile minimizing the co omplexity of the design. LiCHI, imaging narrow contig guous bands in i four spectrral do omains at once, presents a trade-off betw ween spatial an nd sp pectral domain ns. This simultaneous imaging capability is sim milar to SMI [30 0][34] but LiCH HI offers an additional advantage off individual tun nability of the channels, c thus enabling e imagin ng in more than four wavelengths. The four spectral bands are a im maged on one de etector chip by using prisms to o deflect the ligght offf-center. The nu umber of spectral channels can be increased in exxchange for deccrease in spatia al resolution. Th he terms spectrral do omains, spectrral ranges an nd channels have h been useed interchangeably throughout t the paper. he remainder of o the article is organized as fo ollows. Section n2 Th prrovides a description of the te echnique and in nstrument desiggn an nd section 3 pre esents the data and results obttained with LiCHI for various appliccations. Section n 4 summarizes the performance off LiCHI and disccusses the adva antages and challenges of usin ng su uch an instrume ent for hyperspe ectral imaging.

2. INSSTRUMENT 2.1. Conncept Figure 1 illustrates the spatial-spectral-temporral trade-off underlyying the LiCHII concept. Figgure 1 (a) show ws a typical datacub be obtained d during a hyperrspectral imaging process, with p two dimension nal images, each with m × n p pixels, with p being tthe number of wavelength baands, resulting iin m × n × p data vaalues. Figure 1((b) shows the complete datacu ube captured using LLiCHI. Each chaannel of LiCHI can be tuned to a unique waveleength independ dently but simu ultaneously witth the other channeels, over its own n free spectral range (FSR). Eaach image in the daatacube is ob btained by tu uning each ch hannel to a waveleength within itss FSR and captu uring an image once all four channeels are tuned. T The time taken to tune the ch hannels is on the orrder of milliseeconds. Figure 1(b) depicts a datacube obtaineed by LiCHI du uring a complete run. Datacub bes for each channeel of LiCHI are o obtained by spllitting the imagges shown in Figure 1(b) into four iindividual specttral channels. E Each image is represeented by λx,y w where the first ssubscript or ‘x’ signifies the quadraant or channel number and tthe second sub bscript or ‘y’ signifiees the spectrall band number. The numberr of narrow contigu uous spectral b bands in each channel can be different becausse each channell is individuallyy tunable, separrate from the other channels. Add ditionally, all channels cover different waveleength ranges, contiguous within each h channel’s bandw width or full wid dth half max (F FWHM) but nott necessarily contigu uous with otheer channels. Th he FSR and FW WHM of the channeels are also diffeerent from each h other. We proopose to imagee a subset of thee wavelength ggroup p, with the chooices for q, r, s, t made to be sufficient sampless of p. So we have q q, r, s and t im mages for the ffirst, second, th hird and the fourth channel, resp pectively. Typiccally, the total number of waveleengths, i.e. q+r+ +s+t will be lesss than p. This reduces the numbeer of images to b be collected in w wavelength spaace. Once the data iis collected, ccomplexity and computation nal cost of ng can be furth her reduced byy eliminating hypersspectral imagin redund dant data. This can be done in n software during data preprocesssing as mentio oned earlier, orr by designing the imaging system m to capture daata only in a few w wavelength b bands based on infoormation aboutt the target to b be studied. Thee design and operatiion of LiCHI is b based on the lattter.

imagerr conceptualizeed by Scientificc Solutions Incc. (SSI) [32]. While the SSI imagerr uses a single etched etalon n, LiCHI uses four seeparate etalons individually deesigned for theiir respective waveleength bands.

Fig. 3. Scchematic of LiCH HI showing the op ptical components ts. Figg. 1. (a) Hyperspe ectral datacube obtained o using ty ypical hyperspectrral im maging systems (b) ( The complete e hyperspectral datacube capturred byy tuning the fourr channels over each e of their wav velength ranges or FSSRs. Hyperspectrral datacubes arre obtained from m each channel by sp plitting each capttured image into o four images along a the red lin nes wh hich represent th he separation betw ween the channeels. 2.22 Design and exxperimental setu up LiCHI is a multich hannel hypersp pectral imaging system designeed y the above proposition that detailed spectrral to test and verify nalysis can be performed p using g a few pre-seleected rather thaan an hu undreds of wav velength bands.. Each spectral channel is mad de off a liquid crystall Fabry-Perot ettalon and is ind dividually tunab ble in wavelength. Fig. 2 showss an example of the typiccal alon. The curren nt realization has h traansmission response of an eta four etalons thatt together cover four ranges in the visible an nd neear infrared reg gions with center wavelengthss (CWLs) of 426 68 Å, 5580 Å, 6330 Å and 7320 Å with w free spectral range (tunin ng 1 Å, 80 Å and 80 Å respecttively. The tunin ng raange) of 30 Å, 130 raange is determin ned by the size of the gap in beetween the plattes off the etalon. The e initial target fo or LiCHI was th he aurora borealis an nd these wavellength ranges and etalon gap ps were selecteed following method ds discussed in [31].

Fig. 4 shows the assembled instrum ment in the lab b. The front objectivve focuses the light into the ssystem and pro ovides a field of view w of ~40º. An off the shelf 40 00 mm f/2.8 N Nikon lens is used aas the collimato or and its purp pose is to limiit the angles going iinto the etalon ns. The etalon cage consists o of the order sortingg filters, polarizers and the eetalon assembly. The filter mosaicc is a set of fourr order sorting filters each witth a different centrall wavelength an nd bandpass fo or each of the fo our channels of the instrument. T The polarizer mosaic is a set of four polarizzers aligned witth the etalons th hat are placed aafter them in the sysstem. The etalo ons have prism m angles built iinto them in order tto focus the ligght from differeent channels on nto different locatio ns on the detecctor. The reimaaging lens used is a 150 mm f/1 Maarshall CCTV leens. The detecttor used is the Neo sCMOS detectoor by Andor. Th his detector waas chosen for itts large chip size (1 16 mm x 14 m mm) and small pixel size (6.5 5x6.5µm2) in order tto give substan ntial chip area tto image from eeach channel while p preserving spattial resolution.

Fig. 4. T The left panel sho ows the complette assembled insstrument with compon nents (a) front ob bjective (b) field stop and other frront optics (c) collimatting lens (d) etaalon cage with order sorting filteers, polarizers he right panel and thee etalon assemblyy (e) reimaging leens (f) camera. Th shows tthe 4-channel tun nable etalon setup p with order sortting filters and polarizeers.

esponse of an ettalon with its frree Figg. 2. Example off the spectral re sp pectral range (FS SR), full width ha alf max (FWHM) and CWL (centrral waavelength). he system’s opttical schematic is shown in fig. 3. The design of Th th his system is ba ased on the Sim multaneous Multtispectral Imagger (SSMI) designed by b Semeter et all [27][30] and the t hyperspectrral

The strrategy required for selection n of optical com mponents is uniquee to such a multtichannel system. The lenses n need to have aperturres large enou ugh to accept aall the light fro om the four etalonss. Furthermoree, the collimatting and reimaaging lenses need tto be chosen ssuch that the image at the ffield stop is reduceed in size to fit one fourth of tthe chip. The im mage size at field sttop is 20 mm x 2 20 mm and is reduced to 7 mm m x 7 mm, by choosin ng the ratio off focal lengths of the collimaating lens to reimagging lens to bee approximatelyy 3:1. Since th he reimaging lens is 150 mm focal length, the collim mator with a fo ocal length of 400 mm m was chosen. ntrol system off LiCHI consistss of a voltage co The con ontroller and a temp perature contrroller. The voltage controllerr is a fourchanneel system with each channel d dedicated to on ne etalon. To form a datacube usingg LiCHI, each fiilter is individually scanned

in wavelength over its FSR by changing the voltage for f reespective channels and captturing a multii-channel imagge. Teemperature alsso acts as an external e stimulu us for the liqu uid crrystals and can disturb the voltage-wavelenggth calibration by b inducing a wavelength shift. The e temperature controller c is useed to maintain the e temperature at a few deggrees above th he am mbient tempera ature.

3.. APPLICATIO ONS LiCHI was initiallly designed an nd field-tested for multispectrral im maging of the aurora borealis [31]. However, a hyperspectrral im maging system like LiCHI can be used for otther applications wiith appropriate e choice of the four imaging wavelength w FSR Rs. LiCHI was tested d in the lab fo or three typicaal applications of maging: plant stu udy, landscape classification c an nd hyyperspectral im an nomaly detectio on. This sectio on presents results from theese tests, further con nfirming that sp pectral region seelection is a vallid method for reduccing hyperspecttral data while still being able to peerform the desirred analysis. 3.1 Multispectral Auroral Imagingg Th he aurora is the e name given to the optical emiissions that occur ass a result of the e physical proccesses happening in the earth h’s up pper atmosphe ere. These emiissions serve as a an importaant so ource of inforrmation aboutt these proceesses and theeir co onnection with the t greater geospace environm ment. The aurorral em missions exhibiit simultaneouss spatial, tempo oral and spectrral vaariability, and therefore t auro oral studies can n benefit greattly fro om simultaneous multispecctral measurem ments of these em missions. Fig. 5 shows a typical auroral speectrum, with th he em mission lines measured by LiCHI marked with h their respectiv ve waavelengths. Multispectral im maging of the aurora does not require th he measurement of the complete spectrum, since the underlyin ng ph hysics allow me easurements off a few line specctra to reveal th he paarameters of th he incident precipitation cau using the aurorra. Th he tunability off the spectral channels can bee used to explo ore sp pecific emission ns and their neighboring n speectral content to un nderstand the underlying u physics. The four channels c of LiCHI arre designed to image the em mission waveleengths shown in figgure 4, i.e. 4278 8 Å, 5577 Å, 630 00 Å, 6364 Å, 73 320 Å and 7330 0Å an nd the neighbo oring backgrou und. The measurements of th he em mission wavelengths are term med on-band meeasurements an nd th hose of the neighboring background are called off-ban nd measurements. The T user could modify the cho oice of the centter waavelength of th he filter in each channel in ord der to study oth her au uroral emissions like 7774 Å, 8446 8 Å, etc. LiCHI was installled and tested at a Poker Flat Research Range in Faairbanks, Alask ka during the winter of 2014. The major ch hallenge presen nted by this ap pplication is low w photon flux in co ombination with h the dynamic nature of the phenomenon p th hat reequires short exxposure times to resolve. Durring this test, th he exxposure times to obtain a sa atisfactory signal to noise rattio (SSNR) were quitte long. Howev ver, LiCHI succeessfully captureed on n-band and offf-band images in the 5577 Å and 6300 Å ch hannels when th he signals were of sufficient brrightness.

Fig.5. Th he spectrum sho owing (a) the aurroral emission lin nes at 4278 Å, 5577 Å and the doubleet at 6300 Å - 6 6364 Å embeddeed in a broad backgroound (spectrum m from Poker Flat, AK, courrtesy of Jeff Baumgaardner, CSP, Bostton University). ((b) the 7320-733 30 Å emission lines in n airglow (spectrrum measured w with the spectrograph HiTIES [33]). Duringg the winter or auroral season n, the aurora do oes not occur every n night and when n it does occur iit is not always of sufficient brightn ness. LiCHI waas operationall for part of one auroral season n until now, so the auroral daataset collected d is not very The data colleccted until now is preliminary in the sense large. T that it serves to dem monstrate the ccapabilities of LiCHI as an imagerr for aeronomiccal studies. Datta samples from m one night are preesented in figure 6. The plotss on the left sid de of each of nels show the LiCHI passban the pan nd (red and grreen colored lines) ooverlapped witth the spectra measured by sspectrograph h Range (blue ccolored line). T at Pokeer Flat Research The tuning of the etaalons causes thee LiCHI passban nd to shift in sp pectral space to makke the desired measurements. In fig. 6(a), the 5577 Å channeel is tuned to caapture the 5577 Å emission liine (on-band measurrement) and th he 6300 Å chan nnel is tuned to o capture the backgrround neighborring the 6300 Å emission lin ne (off-band measurrement). In fig. 6(b), 6300 Å ch hannel is tuned d to make onband m measurement aand 5577 Å chaannel is tuned tto make offband m measurement. In fig. 6(c), bo oth channels aare tuned to capturee the off-band ssignal. These daata samples willl serve to aid furtherr design modiifications to optimize LiCHI for certain aeronoomical signals aand to determin ne the best wayy to use this instrum ment in aerono omical studies. This applicatio on has been discusssed by the auth ors in further d detail in [34]. 3.2 Plannt Study Hypersspectral imagin ng is widely ussed in land covver analysis. One off the aspects of this application n is the spectraal analysis of plants. Spectral analyysis of light reflected by the pllants reveals mation which tthe human eyee cannot perceive. In the inform visible portion of th he spectrum, the spectral ssignature is dominaated by absorp ption effects frrom chlorophylll and other pigmen nts. Unhealthy or dry vegetaation or leavess have been

ob bserved to have e a higher reflecctance in the vissible (5000-600 00 Å)) region than th heir healthier co ounterparts [13][35].

a images from an a auroral event, on April 17th 201 14, Figg.6. Shown here are wh hich was capture ed by LiCHI in th he 5577 Å and th he 6300 Å channeels wiith exposure tim me of 30 seconds. LiCHI made on--band and off-ban nd measurements in both b channels. Th he 3 panels labelled (a)-(c) show an au uroral event as it was captured by y three instrumeents – LiCHI, Digital Meeridian Spectrogrraph (DMSP) and d the Digital All Sky Camera (DASC), alll co-located at Po oker Flat Researcch Range. Each panel p shown abo ove co omprises three fig gures. The left fig gure in each paneel shows the DMSP measurements and d the passband of o LiCHI. The figu ure on the top rigght in each panel show ws the image cap ptured by the DA ASC with the blaack bo ox showing the LiCHI L field of view w. The figure on the t bottom rightt of eaach panel shows the t correspondin ng image captured d by LiCHI with the t ch hannels marked in yellow and th he DMSP slit possition marked by ya do otted white line. The T etalons are tuned t (a) on ban nd in 5577 chann nel, off ff band in 6300 ch hannel (b) off ban nd in 5577 chann nel, on band in 6300 ch hannel (c) off band d in both the chan nnels. A potted plant, such as show wn in figure 7(a), having bo oth heealthy and unhe ealthy leaves wa as imaged in the lab using LiCH HI. A multichannel image of this sccene, at one sett of four spectrral baands, is shown in i figure 7(b). The T objective of this experimeent waas to determin ne whether th his system could be useful for f seeparating health hy and unhealth hy vegetation. The images fro om th he four channelss were co-registtered to form one o hyperspectrral cu ube. The spectra al signatures sh howing the refleectance values for f bo oth types of le eaves in fig. 7(c) show expeccted comparativ ve vaalues. As can be seen from the spectral respon nse, a healthy leeaf

can be distinguished ffrom an unheallthy one by justt using a few section ns of the spectrum. In fig. 7(b) the bottom edge of the image iin the 7320 chaannel is seen to overlap the top p edge of the 6300 cchannel image. This is becausse of a slight m misalignment error iin the plate an ngles of one o of the etalons resulting in insufficcient diagonal sshift of the imagge from that chaannel.

Fig. 7. (aa) A potted plantt in the lab. (b) A multichannel im mage of a plant, with eaach channel tuned d to a certain waavelength within its respective FSR, cap ptured by LiCHI. (c) Relative specctral response off a healthy leaf and an n unhealthy leaf,, with spectra fr from each chann nel shown as separatte panels. There are two ways to analyze an nd understand data in this applicaation. One meth hod is to study tthe spectra from m individual pixels aand use it to deerive informatiion about certaain objects in the imaage, which in th his case are heaalthy and unhealthy leaves. The oth her method is to use clusterin ng to automaticcally classify the objjects present in the image. F Figure 8 showss the images obtaineed as a result o of using k-mean ns clustering[36 6][37] on the hyper sspectral image cube captured by LiCHI. As th he number of

clu usters increase e, the classificattion based on spectral s respon nse beecomes more sp pecific and it becomes b possib ble to classify not n just the targets but b features within certain taargets as well. In figgure 8, when th he number of clu usters is 4, a broad classificatio on distinguishing be etween leaves and a the box can be seen. With 10 1 an nd 20 clusters, differences d betw ween the leavess of the plant, th he wrriting on the wall, lines on the e pot are classiffied separately as weell.

algorith hm used in thiis work clusterrs the pixels in n the spatial domain n on the basis of the distancces between th heir spectral values,, i.e. it groups ttogether pixelss, in the image, which have similarr spectra. 3.2 Lanndscape classificcation In a diffferent test, a ciity scene was im maged using LiC CHI. This city scene cconsisted of vaarious man-maade objects and d trees. This scene iis shown in truee color in Figurre 9(a). Figure 9 9(b) shows a hannel image o of the scene aas captured byy LiCHI and four-ch Figure 9(c) shows onee frame of the d data cube. The k-m means clusterin ng algorithm usses the spectral information provideed by LiCHI and clusters the vvarious objects scene in the image into differen nt categories based on theeir spectral signatu ures. The numb ber of clusters can be chosen d depending on the diffferent objects present in a scene. Since a tree has a spectraal response diffferent from a building, both h the objects will bee classified into separate caategories by tthe k-means algorith hm. This algoriithm is especiaally useful when n separating man-m made objects frrom natural targets. For our test, the kmeans algorithm wass applied with seven clusterss to the city scene sshown in figurre 10. The treees were clusterred together and th he man-made objects weree classified in nto different clusterrs depending on their spectraal responses. T These results are shoown in figure 10 0.

Fig. 9. (aa) A city scene w with buildings and d trees, used for tests of LiCHI, shown h here in true colo or. (b) An image, from one spectraal band, of the city scen ne from the prevvious figure, captu ured in four chan nnels by LiCHI. (c) Onee frame from th he data cube forrmed after co-reegistering the images from the four chaannels.

Figg. 8. An image of the plant at one spectral s band from the hypercubee is sh hown here along g with the resultts of clustering using u the k-meaans method with differrent number of cllusters. The leavees in general can be seeen classified sepa arately from the manmade m objects. As the numberr of clu usters increase, the different kin nds of leaves caan also be seen as claassified separatelly. Th he k-means alg gorithm is a clu ustering algoritthm. For a set of ob bservations (λ1, λ2, λ3 … λn), where w each observation is a qdimensional ma atrix or vectorr, k-means clu ustering aims to paartition the n ob bservations into o pre-specified k sets S = {S1, S2, S3… Sk), so as to minimize the sum s of squares within a clusteer. Th he aim of the k-means algorithm is to find

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μ ||

∈

W Where µi is the mean of the points p in Si. In our algorithm mic im mplementation, the data is thrree dimensionaal, i.e. it has tw wo sp patial dimensions and one spectral dimensio on. The k-means

Figg. 10. The result of o the k-means allgorithm to analy yze the city scenee is sh hown in this figu ure. As can be seen in the pictture, the trees are a clu ustered together,, i.e. shown in ora ange in this imagee and the rest of the t maan-made objects are clustered se eparately accordin ng to their spectrral responses. 3.33 Anomaly dete ection An n anomaly or outlier is a sm mall subset of datapoints in a daataset that is diffferent from the e general trend d of the rest of th he daataset. In opticcal/image data, an anomaly is usually a pixxel wh hich is distinct from the rest of o the image ow wing to its spatial, sp pectral, temporal or polarimettric properties. For initial tests, an n anomaly in th he form of a kry ypton lamp was introduced in na wiinter city scene e. Figure 11(a)) shows a true--color image, an nd Figure 11(b) sh hows the scen ne as captured d by LiCHI. Th he sp pectral informattion about the scene was proccessed through ha co ommonly used anomaly detecction algorithm m, the RX or r(X) alggorithm[3][38][39]. This is an adaptive matched filtter technique freque ently used to de etect anomalies in hyperspectrral im magery. This alg gorithm can be e used to detecct a target whose sp pectral signaturre is known, and also for an nomalies with no n prreviously known spectral signa ature. Th he four quadran nts of the full im mage were co-reegistered to forrm four separate im mages each belonging to a diffeerent wavelenggth raange. A datacub be was formed d by combiningg all four sets of im mages together and a another sett of four datacu ubes were formeed eaach correspond ding to the fo our wavelengtth channels. For an nomaly detecttion, the fou ur datacubes were analyzeed seeparately and then in com mbination by using a-prio ori information abou ut the anomalo ous object. For the purposes of th his demonstrativ ve test, we used pen ray lamp ps as outliers in na citty scene. The first f test was with w a Krypton lamp. As can be b seeen from the fig. 11(c), only a fe ew spectral ban nds are enough to reecognize the an nomalous specttral response. This T is especiallly ap pplicable if the e spectrum of the target is known a-prio ori. Kn nowledge of th he target specttrum can also be exploited to further reduce th he amount of data d carried th hrough into posstprrocessing. In nstead of analyzzing the data from f all the ch hannels, only on ne ch hannel can be evaluated. e As in n the above exaample where th he krrypton lamp ussed is known to t have an emiission line in th he 55 500-6000 Å ran nge, data from that channel alo one could be useed to find the anom maly. Figure 12 1 shows the result r of the RX R deetection algoriithm used on n one channeel of LiCHI to au utomatically find the krypton lamp as an anom maly based on its i sp pectra. In n another test of LiCHI for anomaly detection n, a lamp of low wer intensity was used. It is a pen ray lamp filled d with Argon gaas. hows a true-co olor image of the argon lam mp Figure 13(a) sh em mbedded within a city scene e, and Figure 13(b) 1 shows th he sccene as captured by LiCHI. The e argon lamp has a spectral lin ne in the wavelength range of the 7320 7 Å channell, and no spectrral lin nes in the wave elength ranges of the other ch hannels. This caan bee observed in Figure F 14, wherre the lamp is not n visible in th he 63 300 channel (ffigure 14(a)) but b can be seeen in the 732 20 ch hannel (figure 14(b)). Since RX R algorithm detects d local an nd glo obal anomalies, applying it in this channel results in detectio on off multiple local anomalies a such h as the clouds and a flag, unlike in th he case of the krypton k lamp, as a shown in figgure 15(b). Thus th here is a need to t further proccess the resultss obtained. Usin ng th he information that t the argon spectral line will be visible on nly in the 7320 channel and not the e other channelss, RX algorithm is pplied to the 63 300 Å channel datacube, and results in clouds ap an nd the flag as an nomalies, as sh hown in figure 15(a). 1 Comparin ng figgures 15(a) and 15(b), the la amp appears only in the latteer. Go oing a step furtther to filter outt the anomaliess we do not neeed, reesults in 15(a) can c be subtractted from 15(b)) and a thresho old

can bee applied to filtter out the noise. This resultts in figures 16(a), before noise ffiltering, and 1 16(b), after noise filtering. Althouggh some parts o of the flag still sshow up as an aanomaly, the lamp n now appears cleearly as an ano omaly in the im mage. Spatial techniq ques can also bee applied to com mpletely filter o out the flag.

krypton lamp Fig. 11.. (a) True colorr image of a citty scene with k embedd ded within the sccene (b) Multichaannel image of a ccity scene with buildinggs, sky, snow aand a krypton lamp, at one spectral band, captureed by LiCHI, wherre the krypton lam mp can be seen in n one channel. (c) Specctra of a pixel taaken from the sn now region and a pixel taken from th he lamp portion of the image.

Fig. 12. Anomaly detecttion using data ffrom only one cchannel of the m one wavelengtth in the 5577 Å channel (b) imager.. (a) Image from result oof the RX anom maly detection aalgorithm with the anomaly presentted in its 2D conteext. Despitee the myriaad computatio onal anomalyy detection algorith hms currently in use, the prim mary method o of identifying an ano maly still is thee inspection of images by a hu uman. In the examplles presented above, in add dition to usingg automated anomaaly detection, th he aim is to maake it easier forr a human to see thee anomaly. Thiss is done by usin ng the spectral information about tthe anomalouss object and capturing imagess with LiCHI tuned tto wavelengthss where the objject is expected d to be most visible,, such as in fiigures 12(a) aand 14(b). Th he algorithm

reesults in figure e 16 can also serve this pu urpose for mo ore difficult cases succh as the argon lamp.

Fig. 16. (a) RX result of 7320 – RX resultt of 6300 (b) Ressults obtained after th hresholding the result in (a) to filter the noise and estimate possiblee outliers. Figg. 13. (a) True color image of a city y scene with argo on lamp embedd ded wiithin the scene (b b) Multichannel image of a city sccene with buildings, sk ky, snow and an argon a lamp, at one e spectral band, captured c by LiCHI.

age of city scene in i fig. 12 in the 63 300 channel, at one Figg. 14. (a) One ima sp pectral band (b) One O image of city y scene in fig. 11 in i 7320 channel, at on ne spectral band. The lamp can be seen in (b) but not in (a).

Figg. 15. (a) Result of RX algorithm on data set from m 6300 channel (b) ( Reesult of RX algoritthm on data set frrom 7320 channeel.

4. Concclusion A tech hnique has beeen presented tto significantlyy reduce the dimenssionality of hyp perspectral dataa during the daata collection processs, thus savingg on computin ng costs, and sstill achieve resultss similar to regu ular hyperspecttral imagers. An n instrument, LiCHI, h has been constrructed and testted to verify thiss method for some aapplications. Altthough LiCHI w was initially con nstructed for multisp pectral imagingg of the aurora borealis, its versatility was demon nstrated by tessting it for otther applicatio ons such as anomaaly detection, p plant study an nd landscape classification. The ressults obtained aafter preliminarry testing of thee instrument and an nalysis of the images are prromising and d demonstrate LiCHI aas a good candid date for hypersspectral imagingg. LiCHI offers an ad dditional advan ntage of simu ultaneity of measurrements in m multiple wavellengths, a feaature which significcantly reduces acquisition tim me and can be eexploited for dynam mic or ephemeraal events. Future work on this instrument shou uld aim at achieving higher sensitivvities, especiallly in the bluee wavelength rregions. The large ssize of the etallons and therefore the instru ument stems from tthe fact that it was constru ucted to have large light collectiing areas in order to be used for photon starved applicaations such as the aurora. B But for other applications discusssed in this pap per, the size caan be significan ntly reduced. The clu ustering and an nomaly detectio on algorithms iin this paper have b been used in their basic fo orms to demo onstrate the feasibillity of using thiis instrument ffor common applications of hypersspectral imagingg, but can be im mproved upon significantly to obtaain better resultts. 5. Acknnowledgements We would l ike to acknowledge Glenn Th hayer at the Boston n University Sccientific Instrumentation Faccility for his fantasttic role in build ding LiCHI. We would also lik ke to express our graatitude to Keviin Abnett and the rest of thee Poker Flat Researrch Range team m for their sup pport during o our research campaiign.

Fundin ng Informatio on Nation al Science Fou undation (NSF)) (AGS 096007 78 and AGS 124467 7).

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