REMOTE SENSING REQUIREMENTS DEVELOPMENT: A SIMULATION-BASED APPROACH

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1 REMOTE SENSING REQUIREMENTS DEVEOPMENT: A SIMUATION-BASED APPROAC V. Zanon a, B. Davs a, R. Ryan b, G. Gasser c, S. Blonsk b a Earth Scence Applcatons Drectorate, Natonal Aeronautcs and Space Admnstraton, Bldg., John C. Stenns Space Center, MS 3959 USA (bruce.davs, vck.zanon)@ssc.nasa.gov b Remote Sensng Drectorate, ockheed Martn Space Operatons Stenns Programs, Bldg. 5, John C. Stenns Space Center, MS 3959 USA (robert.ryan, slawomr.blonsk)@ssc.nasa.gov c Informaton Systems Drectorate, ockheed Martn Space Operatons Stenns Programs, Bldg., John C. Stenns Space Center, MS 3959 USA gerald.gasser@ssc.nasa.gov Commsson I, WG I/ KEY WORDS: Smulaton, yper spectral, Multspectral, Resoluton, Radometrc, Regstraton, Spectral, Requrements ABSTRACT: Earth scence research and applcaton requrements for multspectral data have often been drven by currently avalable remote sensng technology. Few parametrc studes exst that specfy data requred for certan applcatons. Consequently, data requrements are often defned based on the best data avalable or on what has worked successfully n the past. Snce propertes such as spatal resoluton, swath wdth, spectral bands, sgnal-to-nose rato (SNR), data quantzaton and band-to-band regstraton drve sensor platform and spacecraft system archtecture and cost, analyss of these crtera s mportant to optmze system desgn obectvely. Remote sensng data requrements are also lnked to calbraton and characterzaton methods. Parameters such as spatal resoluton, radometrc accuracy and geopostonal accuracy affect the complexty and cost of calbraton methods. owever, few studes have quantfed the true accuraces requred for specfc problems. As calbraton methods and standards are proposed, t s mportant that they be ted to well-known data requrements. The Applcaton Research Toolbox (ART) developed at the John C. Stenns Space Center provdes a smulaton-based method for multspectral data requrements development. The ART produces smulated datasets from hyperspectral data through band synthess. Parameters such as spectral band shape and wdth, SNR, data quantzaton, spatal resoluton and band-to-band regstraton can be vared to create many dfferent smulated data products. Smulated data utlty can then be assessed for dfferent applcatons so that requrements can be better understood.. INTRODUCTION The accuraces and specfcatons of remote sensng data wll determne the desgn and cost of a remote sensng system. Parameters such as sgnal-to-nose rato (SNR), ground sample dstance (GSD) and data quantzaton wll mpact sensor desgn, data storage, communcatons, and processng archtectures and costs. Remote sensng calbraton and characterzaton methods and nstruments are also drven by the accuracy of the data beng studed. For example, an absolute radometrc accuracy requrement of 3 percent n an mage dataset wll drve the need for the same level of calbraton accuracy for both laboratory and vcarous calbratons. Ths wll n turn affect the cost and complexty of calbraton and characterzaton approaches and procedures. A thorough understandng of requred calbraton accuraces s therefore requred before developng and performng calbraton procedures. Parametrc studes through data smulaton can help to optmze data requrements pror to nstrument desgn or data acquston. In addton, physcs-based smulaton can offer an addtonal role n the cross-comparson verfcaton and valdaton of remote sensng systems. The NASA Stenns Space Center (SSC) n Msssspp has developed the Applcatons Research Toolbox (ART), a group of data smulaton algorthms desgned to support systematc studes of remote sensng data requrements. The ART software provdes the capablty to generate smulated multspectral mages wth predefned propertes from exstng data wth hgher spatal and spectral resoluton. Multple datasets smulated wth key data characterstcs vared parametrcally can be then evaluated by potental end-users for utlty n real-world applcatons.. FUNCTIONA OVERVIEW The ART data smulaton process begns by dentfyng an nput dataset. Typcally, the nput data s very hgh spatal resoluton hyperspectral or multspectral magery. The frst step n the data smulaton process s spectral band synthess, whch s the process of combnng several hyperspectral bands to create one multspectral band. In the next step, band-to-band msregstraton artfacts may be added to smulate sensor artfacts ntroduced durng data acquston. Next, the spatal degradaton or spatal synthess algorthm s appled to convert the nput mage GSD and/or pont spread functon (PSF) to the GSD and PSF of the targeted sensor. Nose may then be added to the smulated mage by applyng a two-pont nose equvalent radance random nose algorthm. Because of the heavy numercal processng, the data precson of the resultant mage s usually not at the smulated sensor s desred level. Therefore, an algorthm s appled to convert the quantzaton of the processed mage (normally 3-bt floatng pont) to the target sensor s quantzaton level. Dependng on the ntent of the smulaton study and on the type of nput data, some or all of these steps may be performed. The next secton descrbes each step n detal (Gasser, ).

2 3. Spectral Band Synthess 3. AGORITMS The smulaton of a wde-band multspectral sensor usng a narrow-band hyperspectral sensor s acheved by usng a lnear combnaton of the hyperspectral sensor responses, normally Gaussan n nature, to create a wde-band spectral response (represented by a spectral response curve). In ths process, each band of a multspectral mage s smulated by a weghted sum of the hyperspectral mage bands. Dfferences between the ART spectral synthess algorthm and other methods are n the ways the weghts are determned (Blonsk et al., ). In the ART approach, calculaton of the weghts s based on fndng the best approxmaton of a multspectral response by a lnear combnaton of the hyperspectral responses. Ths method s consstent wth the goal of accurately modelng a sensor wth a predefned spectral response. To llustrate, consder a multspectral nstrument () wth N bands and a hyperspectral nstrument (SI) wth N SI bands. The spectral response of the th band R s defned at n wavelengths λ k. Spectral response of the th SI band R SI s also known for these wavelengths. The lnear combnaton coeffcents c are derved by solvng the followng set of bandsynthess equatons n the least-squares sense: R NSI SI cr ( λ k) ( λk ) for k,..., n;,..., N () Spectral responses of exstng hyperspectral nstruments, such as the Arborne Vsble/Infrared Imagng Spectrometer (AVIRIS) and yperon, are accurately approxmated wth Gaussan functons. For the SI bands wth the Gaussan shape and full wdth at half-maxmum, the coeffcents c are used n the followng weghted-sum formulae to calculate (for each pxel) spectral radance of the syntheszed multspectral mage bands from the hyperspectral radances SI : NSI c NSI SI c for,..., N () Spectral Response Wavelength [nm] Fgure. Comparson of actual (dashed) and smulated (dotted) spectral response of andsat 7 ETM+ band ; hyperspectral AVIRIS components used n the band synthess are shown as sold lnes. 3. Band-to-Band Msregstraton The ART provdes a mechansm for ntroducng the effects of band-to-band msregstraton nto smulated mage products. The msregstraton method allows users to shft pxels of the band of nterest n the column (left/rght) and/or row (up/down) drectons. A new mage s created that s clpped by the number of rows and/or columns shfted. For the band of nterest, clppng takes place on the mage edges n the drecton of shft, and all other bands are clpped on the edges opposte to the drecton of shft. When more than one band s shfted, the output mage s the unon of all clpped bands. It s recommended that spatal synthess/degradaton (descrbed n the followng secton) be performed after the band-to-band msregstraton procedure. Fgure llustrates a smple example where only one band s shfted. The resultant mage wll always be smaller than the orgnal mage. Ths fact must be taken nto account when comparng the msregstered mage to the orgnal mage (Gasser, ). Orgnal Image Orentaton andsat7 : Band (Blue) n Resultant Image An example of the band synthess s shown n Fgure for the case of andsat 7 Enhanced Thematc Mapper Plus (ETM+) Band smulated from AVIRIS 999 data. The fgure also llustrates that although the syntheszed bands and the actual bands closely overlap, some artfacts do occur, such as rpples at band plateaus, shoulders at band edges and negatve values outsde bands. Applyng the ART spectral band synthess approach to an AVIRIS mage produces a smulated mage whose radance values are consstent wth the radance values of a concdent andsat 7 mage when atmospherc radatve transfer effects are taken nto account (Blonsk et al., ). m n-c m-r Band of Interest Shfted down r rows and rght c columns Fgure. Band-to-band msregstraton for a sngle band.

3 3.3 Spatal Resoluton Smulaton Spatal resoluton smulaton processng converts the nput mage s GSD to the desred sensor s GSD. Ground samplng dstance refers to the sze of an mage pxel and s a functon of the nstantaneous feld of vew (IFOV) and alttude of the sensor. It should be noted that n-track and cross-track pxel sze s not necessarly the same, partcularly for sensors wth a wde feld of vew. The ART provdes two methods of spatal smulaton: spatal pont spread functon synthess and spatal degradaton (Gasser, ) Pont Spread Functon Synthess: The smulaton of a sensor wth a low-resoluton GSD usng a hgh-resoluton GSD sensor s acheved by usng a lnear combnaton of the hgh-resoluton sensor s pont spread functons (normally Gaussan n nature) to create a low-resoluton pont spread functon. The algorthm s smlar to the spectral synthess algorthm dscussed n secton 3., but t s extended to two dmensons. The coeffcents are now c kl, where the and subscrpts are the number of rows and columns of the hghspatal-resoluton mage and the subscrpts k and l are the number of rows and columns of the low-spatal-resoluton mage. Consder the low-resoluton sensor andsat 7 and the hgh-resoluton sensor AVIRIS. Assumng the PSFs are separable n the X and Y drectons, the followng equatons can be used to express an effectve PSF of the andsat mage as a lnear combnaton of the AVIRIS mage PSF: PS F ( x x,) b PSF ( x x ) (3) l l X PS F (, y y ) a PSF ( y y ) (4) kl k l k k Y c a b (5) These coeffcents c kl are found ndependently for each andsat pxel by solvng, n the least squares sense, the equatons (3) and (4) for a gven set of ponts (x, y). In current ART smulatons, the PSFs are modeled wth two-dmensonal Gaussan functons. The range of summatons n the equatons s lmted to the regon n whch each of the PSF components exceeds a threshold value. In the ART, the threshold s currently set to. ( percent). Applcaton of ths algorthm for one of the pxels n the low-spatal-resoluton andsat mage generates plots of the horzontal and vertcal pont spread functons shown n Fgure 3. For each spectral band, the andsat mage (') s smulated by lnear combnaton of the AVIRIS mage () pxels accordng to equaton (6). kl c kl c kl Ths PSF smulaton process s a computatonally ntensve operaton, even for moderately szed mages. Usng reasonably (6) equpped computer hardware, processng has taken up to several hours per band for typcal mages (Gasser, ). Pont Spread Functon Pont Spread Functon Fgure 3. ART PSF synthess example showng andsat 7 PSF (dashed), AVIRIS PSF components (sold), and smulated andsat (dotted) Spatal Degradaton: As an alternatve to the hghly computatonal process descrbed above, t s useful to have a hgh-speed spatal smulaton algorthm that can produce results n a tmely fashon. The ART spatal degradaton algorthm uses a combnaton of low pass flterng (PF) and resamplng technques to smulate spatally the desred mage product. ow pass flterng s acheved by convolvng an mage wth an averagng kernel of M N dmensons (usually square). Ths allows the user to create an mage wth the blur characterstcs of the desred sensor (Gasser, ). Resamplng s performed by choosng a sngle pxel from an N N block of pxels wthn the mage to represent that entre block of pxels (Gasser, ). 3.4 Nose Vertcal drecton [m] orzontal drecton [m] The ART nose algorthm s based on the two-pont nose equvalent radance (NER) model shown n equaton (7). To calculate NER for an arbtrary nput radance ( ), NER values (NER and NER ) must be suppled for each band of the sensor at two radance levels: low ( ) and hgh ( ).

4 NER( ) NER NER ( NER NER ) Nose s smulated by addng to each band of the radance mage a matrx of random numbers wth normal dstrbuton, zero mean, and standard devaton equal to the NER calculated specfcally for each pxel (Gasser, ). (7) b m.996 R NER( ) rnd( µ, σ ) (8) 3.5 Data Quantzaton Data quantzaton refers to the process by whch data wth one precson s converted to data wth another precson, usually lower than the ntal precson. Ths allows the user to convert the smulated data set to the data quantzaton level and data storage type of the desred sensor. For example, mage pxels wth an effectve precson of 4 bts and stored as 3 bt floatng pont numbers can be converted to mage pxels wth a precson of bts and stored as 6 bt ntegers. Note that the N-bt mage can be stored n more than N bts (for example, a -bt mage stored as 6-bt ntegers). In such a case, the maxmum mage value s not the largest nteger value of the storage unt, t s based on the value of the maxmum dgtal number, DN max (or maxmum radance, max, for radance mages). Data quantzaton n the ART s accomplshed usng the equaton lsted below. ( ) DN DN max mn DN DN mn + (9) mn max mn INT DN mn, DN max, mn, and max represent the mnmum dgtal number, maxmum dgtal number, mnmum radance, and maxmum radance values, respectvely, and INT ndcates the truncaton to an nteger functon (Gasser, ). 4. EXAMPES 4. ART Valdaton Usng andsat The ART algorthms and selected smulated products have been valdated usng actual andsat 7 ETM+ scenes acqured nearly concdent to the nput dataset. In one example, an AVIRIS scene of the Department of Energy Savannah Rver Ste n South Carolna acqured on July 6, 999, was used as an ART nput dataset for smulaton of several andsat-lke products. The AVIRIS data, acqured at approxmately 3-meter GSD, had ART spectral, spatal and nose algorthms appled. Several varatons of spatal degradaton were explored usng dfferent combnatons of low pass flterng and resamplng. Smulated products were then compared to andsat 7 ETM+ data acqured near concdentally. The resultng radance scatter-plots for two of the products and for ETM+ are shown n Fgure (a) 3-meter PF appled at -meter ntervals (-meter GSD) b m.488 R (b) 3-meter PF appled at 3-meter ntervals (3-meter GSD) b m R (c) andsat ETM+ Fgure 4. Comparson of radance scatter plots for ART smulated data (a and b) and for andsat 7 data (c), andsat band vs. band. 4. Sensor Cross-Comparsons ART smulatons have been used to perform sensor crosscomparsons for performance characterzaton. IKONOS mages were used to smulate four VNIR bands of andsat 7 ETM+ mages. Both IKONOS and andsat 7 mages were of smlar processng level (radometrc correcton, georeferenced wth cubc-convoluton resamplng, UTM proecton) and were acqured on June 3,. For each spectral band, the andsat 7 mage was smulated by a lnear combnaton of the IKONOS mage pxels, as descrbed n secton 3.3., to create a smulated andsat product. Smulated products were compared

5 wth the concdent andsat 7 mages to provde nsghts on radometrc calbraton, spatal resoluton and geolocaton accuracy of the IKONOS mage products. Slght dfferences between IKONOS and andsat 7 spectral bands were not addressed. To perform radometrc comparsons, IKONOS ntal postlaunch radometrc calbraton coeffcents were appled to the IKONOS mage pror to spatal synthess. The resultng smulated mage radance values were compared to the wellcalbrated andsat 7 radance values. The results revealed an nconsstency between IKONOS and andsat 7 radometry as shown for the NIR band n Fgure 5. Ths smulaton valdated smlar results derved from vcarous calbraton methods. The IKONOS radometrc calbraton coeffcents were subsequently updated (Blonsk, ). AVIRIS Earth Scence and Applcatons Workshop, March 5-8, Pasadena, Calforna. Gasser, G.,. Applcatons Research Toolbox Technology Overvew. NASA Earth Scence Applcatons Drectorate, John C. Stenns Space Center. ACKNOWEDGEMENTS Ths work was supported by the NASA Earth Scence Applcatons Drectorate under contract number NAS 3-65 at the John C. Stenns Space Center, Msssspp. Fgure 5. Radance scatter plot showng IKONOS-derved andsat smulaton radance values usng orgnal IKONOS radometrc coeffcents vs. actual concdent andsat ETM+ radance values for the NIR band. 5. SUMMARY The Applcatons Research Toolbox, developed at NASA s Stenns Space Center, provdes an ablty to smulate remote sensng data to assst sensor desgn and applcatons trade studes. Through smulaton, data requrements can be assessed aganst applcaton and research needs. Such data requrements analyss not only affects desgn but also has mplcatons for sensor and data calbraton and valdaton. Through smulaton, data of varyng radometrc, spatal and geometrc parameters and accuraces can be produced. The varous smulated products can then be assessed aganst a seres of applcaton needs. Such assessments, combned wth cost/prce consderatons, can help to optmze a sensor s desgn, operatons and calbraton. REFERENCES Blonsk, S.,. IKONOS-based Smulatons of andsat 7 VNIR Data: Comparson wth Actual, Concdent Images. In: Proceedngs of the gh Spatal Resoluton Commercal Imagery Workshop, March 9-, Greenbelt, Maryland. Blonsk, S., Gasser, G., Russell, J., Ryan, R., Terre, G., Zanon, V.,. Synthess of Multspectral Bands from yperspectral Data: Valdaton Based on Images Acqured by AVIRIS, yperon, AI, and ETM+. In: Proceedngs of the

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