DTIC ADA419452: A Spectrum-Matching and Look-Up-Table Approach to Interpretation of Hyperspectral Remote-Sensing Data

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A Spectrum-Matching and Look-up-table Approach

tu Interpretation of Hyperspectral Remote-sensing Data

Curtis D. Mobley
Sequoia Scientific, Ine.

2700 Richards Road, Suite 107
Bellevue, WA 98005

FINAL REPORT
Prepared for the
Office of Naval Research,
under
Contract No. N00014-00-D-016 1/0001

January 2004

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Oige Naval Roch

S09 Nenh Quincy Suset.

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TE RESTRA ian a Wire]
A spectruneamatching sn4 look-up-table (LUT) methedology has been developed and evaluated for extyacting
‘environmental infomation from remotely sensed hyperspecial imagery. The LUT methodology works as
fellows. Fist a database of rcmote-sensing relleclance (Rq) Hpectra corespenidiny lo various waler depths,
‘boule vellectance spectra, and water-column inherent opticul properties (1OPa) is constructed using a special
version of the tlydrolight radiative transfer numerical model. Secoud, Ihe Re spectrum for x patcular image
viel ix compared with each spectrum inthe database and the closest match #o the image spectrum is founc using
saleas-syuares minnaization, The environmental conditions in nary nce then assim to be the same as the
input conutitions that generated the closest-matching Hydrolight-gonerated spectrm in the database. ‘The LUT
methodology has bern eviluated by application to an Ocean PHILS (Portable Hyperspectral Imaging Low-Light
Spectrometer) image acquited near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottoin
depths were on avcrage within 4% and 0.5 m of independently obtained aroumtic leptin. The LUT-retvieved
‘bottom classification was in qualitative agreement with diver and video spat classification of boltem Lypes, an
the LUT retrieved JOPs wore consistent with TOPs measured at nearby times. and locations.

14 SUBJECT TERE TE RBER OF EES
Etokrup-table, CUR, iyperepectral, remoze sensing, sydrol=ahe

TH SECURITY EL AEACATIN |W GECIMITY CEASSIIOATION [Th SECURITY CLASSIFICATION — Bi LINTANOR OF ABSTRACT
OF nero or tug Peat ‘or anermocr Infiagent
CCU seTPTED DRCLASSTFrED UONCUAESSE TES
A Spectrum-Matching and Look-up-table Approach

to Interpretation of Hyperspectral Remote-sensing Data

Curtis D. Mobley

Sequoia Scientific, Tne.
2700 Richards Road, Suite 107
Bellevue, WA 98005

FINAL REPORT

Prepared for the
Office of Naval Research
under
Contract No. NO014-00-D-0161/0001

January 2004

siuors

1
xecutive Summary

A team of investigatorsledby C. Mobley of Sequoia Scientific, lnc. has developed anilevalwated
anew methodology for extracting environmental infoumation frown rentely scused hyperspoctral
imagery, In bret, this lok up-twhle (LUT) methadology works as follows:

Fitst, a datsbase of remote-sensing reflectance (R,) speciea comesponding various water
depihs, bottom reflectance specta, watcr-coluinn inherent optical properties (OFs), sky conditions,
tal viewing goometries is asverbled. This dulabase is constructed using the aspoeial version of the
Hydofight radiative transfer nurserical model, which provides an exact solution of the unpolarized
redintive transfor equation for ihe given inpot, Each Hlydrolight-generated &, spectrom in the
databuse is tagged by indices that identify the bottom depth, botiom reffectance spectrom, water
1OPs, sun zenith angle, ete, that were used ay input fo the Mydrotight ma, At 3 reiniramm, this
sdarabase should conixin 8, spectra generated for environmental conditions close to thuse ocurting
fn nature atthe tine and location where the image wes acquired. The database also may ectain
spectta corespendling to enviranniental eonditions mueb different trom those of the image: onder
cconsidertion,

‘Second, the R,, spectrum for a particular image pixe! iy compared with euch specteum in the
dtsbase and the closest match Lo the image specteum is found using a least-squares minimization,
‘Thocaviroamental conditions in oatare ae thea assumed to be the surme a che inp conditions that
gonctated lhe closest-malching Hydrolight-generated spectrum inthe database. Thus, the inversion
ff a measured R, spectrum to obtain the eatresponding ewviwoomental conditions is effeoted by a
lable look-up of the conditioas corresponding tothe closes!-matching database spectcum,

Finally, for example, the index tag ientfying which bottom reflectance spectrum was used ia
she closest-rmatching Hydrotight au can he used to idontify the hottom eypest hut pixel, ort obtain
‘other information such 4s hebottom reflectance at a particular wavelength. This processis repeated
for cach pixel inthe image to generate carresponding maps of bottom deplh hottomn type, or wulee-
col FOP,

“The LIT methodology has been evalusted by application o 9 PHILLS (Portable Hyperspectral
Innaging Low-Light Spectrometer) image aequired near Lee Stocking Isind, Bubaruas, on 17 May
2000, ‘The LUT-tctrieved bottom depibs ware compared wich independently ebiained accustic
bathymetry. On average, the LUT deplhs were within 5% unl 0.5 m of the acoustic depths. The
LUT-rotrieved hottam clussification was in qualitative agreement with diver and vidoo spot
classificalion of bottom types, although detailed bottom classification dulu were not available for
(quantluive, pixelby-pixe} comparison with the LUT retrievals, The 110T-retrieved [Ors were
consistent with TOPs measured at nearby times and focstions.

“The detailed discussiom ofthis Work i presented here asa paper which has heen prepared for
submission lo Apalied Opeics in January 2008,
Paper prepared for submisslon to Applied Optics

A Spectrum-Matching and Lookeup-table Approach
to Interpretation of Hyperspectral Remote-sensing Data

Curis D. Mobley
Lydia K. Sundman
Sequoia Seicatific, Ta.
2700 Richards Road, Suite 107
‘ellewue, WA 98005

Contiss 0. Davis
1. Velerie Downes
Robett A. Leathers

‘Maroas Monles

[Naval Research Lahorary

4555 Overlook Ave, 3.8

‘Washington, D.C. 20375

W. Pant Bissett
David D.R. Kohler
‘Florida Environmental Research Taslitule
4807 Bayshore Blvd, Soite 104
Tampa, FL 33611

R. Pemmela Reid
Esie M. Touchard
Rosenstiel Schoul of Marine and Atmospheric Seienee
University of Miami
4600 Rickenbacker Causeway
“Miami, HL 33149
. Introduction

Recon yoarshave een much meres the development of hyperspoctal imagers ns in he analysis
of hypempectral imagery of optically shallow waters. Sensors include the airborne AVIRIS
Caisbome Vivble sad tnfaRed Imaging Spectometes, Occan PAILLS (Ocean Porthle
TMypesepectal Imager foe Low-Lighl Spectroscopy), CAST (Compact Aintome Speeuonttor
_umtger), AAT (Advanced Sihon: Hyporspecte)Luaging System), and Fy Map (Hyperspectal
Mogpe)instraent, andthe sll boene Hyperion imager

Recent applications of hyperspectral imagery have been que vatied, Hochherg and Atkinson (2000)
used AAFIS imagery und Andséfougt et al. (2003) uscdl CAST imagery for mapping and
classification of benthic types into corals, algae, and sediments by Fourth-derivative analysis of
remotely sensed rfleetance spectes, Dierssen etal, (2003) used spectral ratios derived from Ocean
HILLS imagery of shallow Bahasoian waters to exteact bathymetry aul bottom typo; Louchard et
1. (21813) used spectrum matching for the same purpose on the same inzagery. Samibye an Holyer
(1958) used a neural network to determine bwhymetry from AVIRIS innugery of Florida watery, and
‘Melack and Gastil (2001) usec! AVIRIS co map phytoplankton concentrations in Mono Lake,
California. In all eases the exploitation of hyperapectil imagery for shallow waters dopends on
being able to cattaxt information sbout water-column optical properties, bathymetry, er hottom type
from remote-nensing reflectance spectra,

“The wembue-sensing reflectance Rs the ratin of Ue waterleaving radiance Zo the incident plane
inradiance £, from the son and haokground sky, both F, aul E, acc cvaluited just above the sea
surface. tn practice, a avast be estimated either by removing the surface-rcflected cafiance from
the tota) (water-leaving plus cucface-rotloctod) radiance measured jst above the surface, or by
extrapolating the upwelling sadionce measured below the sea surface through tbe surface.
Ragaidicss of how itis obtained from fleld mersurements, R, is uniquely determine by de water
‘column inietent optical peopertes (OPS, narvely the absorbing ana scatleiug properties ofthe water
body), the depth and bidircetiomal reflectance distribution finction (RROF) of uve bottom, the sun
and sky tidianee incident onto the sea surface, and the wea surface wave stale. Given compere
{information about these eivirowunental conditions, X, oan be computed exactly by namerically
solving the tafative transfer equation (RTF). The solutionof this forward radiative transfor problem)
‘ean bc obtained using the Hydrolight mdiative transfer software package (Sequoia Scientific, Inc.)

(the extraction of environmental information from measured sefleclunce spectra constitutes #
sadiative-transfer iaverse problema, which is discussed inthe present paper. Thvere prublems 278
notoriously difficult because of potential non-uniqueness problems. Although a given R,,spectnm
"uniquely corresponds toa particular set of environmental concitions, tors ia the measured R, may
ause a particular A, specisum to be associated with incorrect environmental conditions when
“averting”, to obtain information abowt the eavironmucnt, Thas itis often necessary to constrain

4
inverse problems so ast guide the inversion to the correct solution, Such constants often take the
formof simplifying sssumptions about the underlying physical or mathematical problem, or afded
‘environmental information.

“We approach the inversion of R, via a spectrum-matching ani levk-up-cable (LUT) methodology
esigued forthe simaltancons extraction of balhymetry, bottom classification, and water-column
sorption und scattcring propesties from hyperspoctral imagery. After presenting the underlying
[LATT ideas, we apply our methadialogy toticexteaction of environmental in[osmation from an Ocean
PHILLS image acquired on May 17, 2000 n apically shallow waters ucarLce Stocking ltand (LSD,
Babaroas. This area has been previously studied, s0 chat acoustic buliymetcy and diver and video
observation af httom type ate available for comparison wilh che corresponding LUT-reuieved
values. ‘We evaluate both uneanstrained and constrained forms of the LX:T methodotogy.

2 Phe LUT Methodology

‘the basic idea underlying the LUT methodology for inverling R, is simple. Fit, adatabase of Ry
spectea correaponding to various water deplhs, bolton reflectance spect, water-column 1OPs, ky
‘conditions, and viewing geometries ix assembled, ‘This database is consimuted using the a special
version of the Hydolight radiative transfer numerical move! (Sequoia Sctomtific, Tue), which
provides an exacr solution ofthe unpolarized RTE forthe given input. Bach Hydrofight-goncrated
2, spectrum iu die datshase is tagged hy indices that identify the hottom depth, hotlonn reflectance
spectaura, water TOPs, son zenith angle, ct. that wete uscd a5 input o the Liydrofight mn. Ata
minimum. this database shonld contain R, spectra generaed for environmental conditions close to
those occurring i nature atthe time and location where the image was acquired. The database also
‘may contain spectra covtesponsting to envizonmental conditions mauch uiferent from those of the
image under consideration.

Sccond, the R, spectrum for aparticular image pixel is compared with each spectrum inthe database
and die closes! match ta the image spectrum iy found. The environmental conditions in nature are
thea assumed to be the same.as the input conditions that generated the closest-malching Tydrofight-
generated speutrurs fn the database. ‘Thus, the inversion of a mewured R,, spectrum to obtain the
‘corresponding cavironttental contilions is effected by a table look-up al the conditions
correspumniding to the closest-raatching datuhase spectrum.

‘Finally, for example, the index tag klenllying which botlom reflectance spectrum was used in the
‘closest. matching Hydrolight an can be used o deny te bottom type at that pixcl, orto obtain
‘ther information such asthe botlom eeflectance al a particular wavelength. This process iscepeated
for cach pixel in the image lo gouerate uoresponding maps of hottom depth, bottom type, or Water.
ccotuman LOPS.
Aldhough spectrom-matehing has « venesable history, previous applications bave ben to easier
problems oF telied on ancillay data, Laboratory or terrestrial applications do, not have the
fgonfounding influence of unknown warer absorption and scattering obscuring the desirod
information, The previows oceanographic application by Louchard et sl, (2003) relied on ancillary
measurements of the IOPs s0 chat the water propertics sould be considered known. In oor
unconstrained analysis inode, we make no @ priort assumptions about the water depth, JOP, or
bottom reflectance.

“The Hiydnolight rans noeded to generate tho daubase are compulationally expensive, bat they are
done only once, Searching the datsbase to find the elosest match to a given imaye spectium is
computationally fast, a is the table Fook up andl generation of graphical er digilal output products,

Spectrum matehi
cdarsbuve spectra vit

is performed using a least-squares comparison of the measured image and

ESO} = YF wR GA) - ROP» a
Bn

sphere (2) iste database speetmm at wavelength band, Rf.) the measured spectra

for uparticolar image pixel, and w(A) is a Weighting fnnetion between O and 1 that cam be used to
‘weight the contribution of differnt wavelength hands (e.g, to discown: wavelengths where. the
smesured reflectance dala ate loss accurate). The smaallost vulue of the teast-squares distance LSQ
determines the closest datshase spectrum jo the measured spectrmn.

(Criterion (1) matches the specteum magnitodes at each wavelength; the simnltancons ineorporaton
‘a xpectal shape information is implicit in cis eritrion. Otber spectrum-matching criteria have
bocon evnsidered. For exaangle, the Special Image Processing Systera (SIPS: Kruso, et at, 1993)
minimizes the angle between the (wo noxunalized spectra in Jedimension:] space, where J is the
number of wavelengths. The SIPS matching critetion consiers rly te spectral shape it regards
twco spectra thal differ only by a multiplicative factor as being a perfect match. Although such «
snatching exterion ts adeqiate for some applications, and raust be used if only uncalibrated spectra
arc available, the SIPS criterion makes no wse of the magnitude information available in the
calibrated PHILS spectra considered here, The LUT metiad uses both magnilude und spectral
shape information to avoid the non-unigueness problems thal often occur when excreting
‘oceanographic information from uncalibrated or nommalized spcetra,
3. Tenagary and Ground Truth
Anesgery

“The Ocean PHILS airborne hyperspeetral imager is a pushbroom-scaning insteament. Teuses a
two-dimensional CCD array with 1024 eross-track pixels for spatial resolution. Light from each
spatial pixel is dispersed onto the.other direction of dhe CCD to obtain (ater binaing) 128 speccrat
channels between 400-and 1000 ne, with anginal handsidih of 4.62, As oormally flown, cach
spitial pixel isoneto twa metens square on dw ground. Davis ex. (2002) give detailed descriptions
of the instrament design and its spectral and radiomelsic calitwation,

(Ocean PHLLLS was flown during the Coastal Benthic Opticul Propetics (CoBOP) field experiment
inthe vicinity of Lee Stocking band (LSI), Bahamas daring May 2000, The wars in the vicinity
of LSTare generally loss than 15 mdecp, bul do extend offshore ta optically deep, open-oocin water.
[Nearshore waters re usually visoally very clear and arc characterized by chlorephyyl emweniations
of 0.1 to 0.2 mg Chl m?, Tn shallow sogions, there is often substaatially more absorption at bln
wavelengths than what would be expected for Caso 1 seaters having the same chlorophyll
concentration, dae to colored dissolved organie matter (CDOM) derived from benthic biota such ws
seagrass and voral or sediment biofilms. Scatcring, however, appears 19 be dominated by
phyluplankton excopt ducing episodic strong winds, which can restapend sediments. The sea hed
onsiats of earbonate sands and barler sediments, scagrass beds, and patches of but hard and soft
corals, The bottom can be tniform on sedles of teus of meters, ur patchy on scales of less Una a
moter, There ae often sharp dividing Lies between bottoms of different lypes, such as between bere
‘sand and dente seagrass beds or coal heads.

Figure | shows a PHILLS RGH image of the Adgerly Cutaren just to the northwest.of TST arquited
a1 0930 local (Eastern Daylight Savings) time (1330 UTM) on 17 May 2000. After grocorection
and discanting questionsble pixels atthe ends. a the scan lies, cae scale image shown here is 900
pixels fom North to South and 1425 picels from East to West. The pixel size is apprasimately 13
‘m,so the image shows un area of approximately 1.2 kin by 1.9 an. This image. contains areas of
highly teflecting ooid sands, sparse to dense seagrass beds, pavements and sediments with varying
degrces of biotin and patchy lurf algae of Sargassum, and stall coral paich reefs. ‘The deepest
ter is about 17m

‘The PHTILLS at-seusor radiances were akmosphetically corrected with Tafa (Montes eta, 2001;
‘Gao-ct al, 2000}, Attaospherie shsorption was modeled using distributions of well-mixed gasses,
appropdate for atropieal atmosphere, The ozone content was set to 0:3 ater-ci (300 Dobson units),
and the water vapor content was set to 3.5 em of precipitable water. A rmaritime aerosol with a
relative humidity of 90% was used the optical propertics of uch an aerosol are described in Showle
‘and Fen (1979), ‘The aetoso! optical depth wae 0.1 at 330 am. Tafkaa uses the wind speed to set

7
Lhe scflcctivity ofthe son surface via the surface slope statistics; Ihe avsiluble speeds are 2, 6, and
10 msl. A-valucof6 ms" tended to overcorcect for atmospheric effects thercby occasionally giving
ogative radiunces at the sex surface, Therefore u speed of 2m s* was uses, which enay Lave Ted 10
underoortection, i, ta water-Leaving cadanoes that were too large

Given an adcurate ratiomettic calibration and using Tafkaa in is aerosol determination mode Cie
aerosols are chosen based on the assumption thatthe water-Ieaving rvlance at several
NIRSWIR wavelengths is U), ‘Tafkaa's lookap-table grid gives water-Jeaving radiomces that arc
accurate tothe equivatent of 20.003 st" in ,. However, the etrors will inreace if, forexamplc, the
true aerosol not closely matched by the meveled aerosol, orif'swells are presencin the immge (Le
the Tue sea-surface geametey is different than the modeled slope statistics in Tafkus). For the
resent image, the aerosols were input to ‘afk rather than being devermined from NIR/SWIR
wavelenglhs, Likewise, the calibration processing was fetly involved (Leatbers et al, 2002). Both
A chese situations may load to larger systematic errors in che final

Bachymerry

‘Acoustic bathymelry in the waters surrounding LST was acquired trom a small boat during 16-20
dume 2061 (awchatd eta, 2008). Bathymetric measurements were recorded ata repetition ras of
10:7 Hz using « Sueaki 852025 echo soundes. and onch depth was recorded along with its time and
the Latins and longitude ws determined by WAAS GPS, The atoustic data were correctectto mean
sea evel (MSL) lo accrtont for tide differences, Extracting oly die acoustic data tat fel within the
latitade-tongitade bounds othe Adderly Cot image of Fig. 1 4of 98,754 depths. The GPS fotcude-
longitade conrtlinatex vere converted lo UTM and then vo image pixel coordinates, ecause of the
slow boat speed and fast surmple sate, piven PITILLS pixcl often conluined several acoustic depth.
‘Multiple acoustic depts within uny PULLS pixel were averaged tc get the depth for thal pixel. The
final result was 20,446 innage pixels for which an acoustic depth is available. These pixels are
shown by the black boat track inPFig.2. The depth conlours of Fig. 2 were genctaled by interpolation
‘of the available depth values using the IDL (Rescarch Systems, luc.) contouring rontine. with
smoothing hy an 1T-pixel boxcar filter G5 piaels to either side of « given pixel)

‘To gain some iden of the accuracy of the fide-carrected acoustic depth, 72 compared the depths for
a pixels where easL-west aud novth-south boat tracks crassel. Jn principle, the MSL depths at the
‘erossings should es Ihe sane for both tracks. which may bave Boon hows apart ‘There were 120
such erossings, including weaustic data from areas not show in Fig. 1, The average difference in
the depths atthe erensing points was 0.1m. with a standard deviation of U.08 m. Only $0 erossing
fad « vifference of more Gian 0-20 ta, aid the largest differenoe was 0.27 m, There was no
ccorrcation between the bottom depth and the difference inthe depth of the crossing racks. Thus
swe feel thatthe aconstie depths are accurate to within 01 406.2 m for dopths of 2 to 12 ma.