The continuing development of new and fundamentally different classes of imaging spectrometers has increased the complexity of the field of imaging spectrometry. The rapid pace at which new terminology is introduced to describe the new types of imaging spectrometers sometimes leads to confusion, particularly in discussions of the relative merits of the different types. In some cases, multiple different terms are commonly used to describe the same fundamental approach, and it is not always clear when these terms are synonymous. Other terminology in common use is overly broad. When a single term may encompass instruments that operate in fundamentally different ways, important distinctions may be obscured. In the interest of clarifying the terminology used in imaging spectrometry, we present a comprehensive system for classification of imaging spectrometers based on two fundamental properties: the method by which they scan the object spatially, and the method by which they obtain spectral information

Boreman, Glenn D.

Sellar, R. Glenn

English

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University of Central Florida STARS Faculty Bibliography 2000s Faculty Bibliography 1-1-2005 Classification of imaging spectrometers for remote sensing applications R. Glenn Sellar University of Central Florida Glenn D. Boreman University of Central Florida Find similar works at: https://stars.library.ucf.edu/facultybib2000 University of Central Florida Libraries http://library.ucf.edu This Article is brought to you for free and open access by the Faculty Bibliography at STARS. It has been accepted for inclusion in Faculty Bibliography 2000s by an authorized administrator of STARS. For more information, please contact STARS@ucf.edu. Recommended Citation Sellar, R. Glenn and Boreman, Glenn D., "Classification of imaging spectrometers for remote sensing applications" (2005). Faculty Bibliography 2000s. 5652. https://stars.library.ucf.edu/facultybib2000/5652 Classification of imaging spectrometers forremote sensing applicationsR. Glenn Sellar,* MEMBER SPIEUniversity of Central FloridaDepartment of Physics12424 Research Parkway, Suite 400Orlando, Florida 32826Glenn D. Boreman, FELLOW SPIEUniversity of Central FloridaCREOL/School of OpticsOrlando, Florida 32816-2700Abstract. The continuing development of new and fundamentally differ-ent classes of imaging spectrometers has increased the complexity ofthe field of imaging spectrometry. The rapid pace at which new terminol-ogy is introduced to describe the new types of imaging spectrometerssometimes leads to confusion, particularly in discussions of the relativemerits of the different types. In some cases, multiple different terms arecommonly used to describe the same fundamental approach, and it isnot always clear when these terms are synonymous. Other terminologyin common use is overly broad. When a single term may encompassinstruments that operate in fundamentally different ways, important dis-tinctions may be obscured. In the interest of clarifying the terminologyused in imaging spectrometry, we present a comprehensive system forclassification of imaging spectrometers based on two fundamental prop-erties: the method by which they scan the object spatially, and themethod by which they obtain spectral information. © 2005 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.1813441]Subject terms: imaging spectrometry, imaging spectrometer; hyperspectral imag-ing; hyperspectral imagery; hyperspectral imager; remote sensing.Paper 040052 received Jan. 28, 2004; revised manuscript received Apr. 9, 2004;accepted for publication Apr. 29, 2004; published online Dec. 17, 2004.Imaging spectrometers are designed to measure the energyor quanta collected from an object as a function of twospatial and one spectral dimension. The resulting 3-Ddataset is often referred to as an object cube or data cube.To be truly an imaging spectrometer, the spatial samplingprovided by the instrument must be essentially continuous;a multiobject spectrograph, which acquires spectra from asparse array of positions across the field of the object, is notan imaging spectrometer in the strict definition of the term,since it cannot provide what would generally be consideredimages. If an absolute radiometric calibration is also pro-vided, then the instrument is an imaging spectroradiometer,though this term is seldom used.Imaging spectrometers used for remote sensing may bedivided into classes based on two aspects: the method bywhich they achieve spatial discrimination, and the methodby which they achieve spectral discrimination. Methods ofacquiring spatial information include whiskbroom, push-broom, framing, and the relatively new class we refer to aswindowing. In our taxonomy, a whiskbroom-scanning in-strument employs a zero-dimensional or spot or point field-of-view ~FOV!, which scans the object in both the along-track and cross-track directions, while a pushbroom-scanning instrument employs a 1-D FOV, which is scannedin only one direction. Typically the scan direction for apushbroom-scanning instrument is the along-track direc-tion, but there are examples of pushbroom instruments thatscan in the cross-track direction.1 While the latter havesometimes been referred to as whiskbroom rather thanpushbroom, the distinction between a zero-dimensionalFOV scanned in two directions versus a 1-D FOV scannedin one direction is the more fundamental distinction, so weclassify all imaging spectrometers with a 1-D FOV as push-broom instruments, regardless of the direction of the scan-ning motion. It should also be noted that it is the shape ofthe FOV that determines the spatial discrimination class; inmost pushbroom instruments this restriction of the FOV isobtained by placing a slit aperture in an intermediate focalplane, but any other method of restricting the FOV to beessentially 1-D will put the instrument in the pushbroomclass.A framing instrument employs a 2-D FOV, which re-mains fixed on the object during acquisition, though mul-tiple acquisitions may be required to cover an object ofgeneral length in the along-track direction. We use ‘‘fram-ing’’ as synonymous to ‘‘staring,’’ but prefer the formerterm since if the scene to be observed is longer than thelength of a single FOV, then multiple frames must be ac-quired and thus framing seems a clearer description. Weuse the term windowing to describe the relatively new classof instruments that employ a 2-D FOV that moves acrossthe object in a continuous fashion in the along-track direc-tion. Windowing is actually distinct from the time-delayintegration technique used by some panchromatic imagers,since in a windowing instrument a distinct exposure is ac-quired each time the FOV moves forward by one groundsample and no integration occurs.Methods of acquiring spectral information include thefamiliar filtering, dispersive, and interferometric tech-niques. Dispersive instruments may use either a prism orgrating. We define the interferometric class as Fourier-transform spectrometers ~FTS!, which are based on two-beam interferometers such as the Michelson, Mach-Zender,*Current affiliation: Jet Propulsion Laboratory, California Institute ofTechnology, Pasadena, CA 91109. Electronic mail:glenn.sellar@jpl.nasa.gov0091-3286/2005/$22.00 © 2005 SPIEOptical Engineering 44(1), 013602 (January 2005)013602-1Optical Engineering January 2005/Vol. 44(1)Downloaded From: https://www.spiedigitallibrary.org/journals/Optical-Engineering on 27 Aug 2019Terms of Use: https://www.spiedigitallibrary.org/terms-of-useor Sagnac. Multiple-beam interferometers such as theFabry-Perot belong more appropriately to the filtering class,as do interference filters, since they obtain spectral dis-crimination by passing at any instant only a narrow rangeof wavelengths, while blocking all other wavelengths.This classification scheme, with examples of commonlyused terms for each class, is illustrated in Table 1. Althoughthere are 12 possible classes in this scheme, we are onlyaware of existing instruments in ten of these classes. Someof these classes are well established and are likely to befamiliar: instruments in the filtering whiskbroom class areoften known as multiband radiometers, the filtering framingclass includes filter-wheel-equipped cameras, instrumentsin the dispersive pushbroom class are often referred to asprism ~imaging! spectrometers or grating ~imaging! spec-trometers, and the interferometric framing class includesthe traditional imaging FTS using a Michelson interferom-eter. Other classes are relatively novel additions to the field.The interferometric pushbroom class2,3 uses static or spa-tially modulated interferometers such as the Sagnac inter-ferometer. The interferometric windowing class4,5 also usesa static interferometer but, unlike the interferometric push-broom spectrometer, does not require a narrow field mask~or slit!. The filtering windowing class6,7 places in an imageplane a filter designed with a passband that varies withposition. The dispersive framing class includes both instru-ments that use an image slicer to rearrange the elements ofa 2-D FOV into a 1-D form at the entrance slit of a prism orgrating spectrometer,8 as well as instruments that usecrossed gratings to produce multiple projections of the ob-ject cube from which the elements of the object cube areestimated using tomographic algorithms.9The classification scheme serves to group instrumentsaccording their fundamental principles of operation, ratherthan by the specific devices employed in the design. Forexample, instruments in the dispersive class may use eithera prism or grating. They are grouped in the same spectralclass because both devices provide fundamentally the samefunction to the instrument: that of sorting rays by wave-length into different angles of propagation. Similarly, in-struments using a filter wheel, an acousto-optical tunablefilter, or a liquid-crystal tunable filter all belong to the samefundamental class: filtering framing. The term ‘‘band se-quential’’ has been aptly used for this class, but the latterterm does not indicate the relationship of this class to othertypes of imaging spectrometers. The term ‘‘filtering fram-ing,’’ however, indicates that this class shares characteris-tics in common with related classes. In common with therelated filtering windowing class, the filtering framing classselects only a single spectral band to reach a detector at aparticular instant, and in common with an interferometricframing instrument, a filtering framing instrument also hasa 2-D FOV that remains stationary on the object duringacquisition.The matrix character of this classification scheme helpsto clarify the principles of operation of novel instrumentsby analogy with more familiar instruments. For example,the interferometric windowing class obtains spatial dis-crimination in the same way the filtering windowing classdoes, while obtaining spectral discrimination in the sameway as the interferometric framing class. Both filteringwindowing and interferometric windowing instruments usea 2-D FOV that sweeps across the object ~typically throughthe motion of an aircraft or spacecraft platform!. In a filter-ing windowing instrument, the detected wavelength variesas a function of position in the FOV, while in an interfero-metric windowing instrument, it is path difference that var-ies with position in the FOV. In the interferometric win-dowing class, the spectrum is obtained by applying aFourier transform to the variation of collected energy withpath difference, in the same fashion employed by the morefamiliar interferometric framing class.This matrix classification approach also helps to distin-guish between instruments that use the same devices butoperate in fundamentally different ways. For example,Sagnac interferometers are used in both interferometricpushbroom instruments and interferometric windowing in-struments, though these two classes obtain spatial informa-tion in fundamentally different ways. Michelson interfer-ometers are typically used in interferometric framinginstruments, while a Mach-Zender interferometer was usedfor the first interferometric windowing instrument.4 In prin-ciple, however, any of these three interferometers may beused for any of the four interferometric classes. ClassifyingTable 1 Classification of imaging spectrometers.Sellar and Boreman: Classification of imaging spectrometers . . .013602-2Optical Engineering January 2005/Vol. 44(1)Downloaded From: https://www.spiedigitallibrary.org/journals/Optical-Engineering on 27 Aug 2019Terms of Use: https://www.spiedigitallibrary.org/terms-of-useinstruments by how they obtain spatial and spectral dis-crimination, as we have done here, rather than by the spe-cific devices ~i.e., the type of interferometer, type of filter,or type of disperser!, provides a more fundamental and use-ful designation.AcknowledgmentsThis research was funded by NASA under grant numberNAG5-10730.References1. C. G. Simi, E. M. Winter, M. M. Williams, and D. C. Driscoll, ‘‘Com-pact airborne spectral sensor ~COMPASS!,’’ Proc. SPIE 4381, 129–136 ~2001!.2. W. H. Smith and W. V. Schempp, ‘‘Digital array scanned interferom-eters for astronomy,’’ Exp. Astron. 1, 388–405 ~1991!.3. L. J. Otten, J. B. Rafert, A. Meigs, B. Jones, R. Prinzing, R. Fronter-house, R. G. Sellar, and P. Hodge, ‘‘The engineering model for theMightySat II.1 hyperspectral imager,’’ Proc. SPIE 3221, 412–420~1997!.4. R. F. Horton, ‘‘Optical design for a high-etendue imaging Fourier-transform spectrometer,’’ Proc. SPIE 2819, 300–315 ~1996!.5. W. J. Slough, J. B. Rafert, C. A. Rohde, and C. L. Hart, ‘‘THRIFTI:Tomographic hyperspectral remote imaging Fourier transform inter-ferometer,’’ Proc. SPIE 3393, 207–216 ~1998!.6. J. C. Demro, R. Hartshorne, and L. M. Woody, ‘‘Design of a multi-spectral, wedge filter, remote sensing instrument incorporating amulti-port, thinned, CCD area array,’’ Proc. SPIE 2480, 280–294~1995!.7. D. C. Reuter, D. E. Jennings, G. H. McCabe, J. W. Travis, V. T. Bly,A. T. La, T. L. Nguyen, M. D. Jhabyala, P. K. Shu, and R. D. Endres,‘‘Hyperspectral sensing using the linear etalon imaging spectral ar-ray,’’ Proc. SPIE 2957, 154–161 ~1997!.8. E. H. Richardson, J. M. Fletcher, and W. A. Grundman, ‘‘Image slic-ers,’’ Proc. of IAU Colloquium 79, 469 ~1984!.9. C. E. Volin, T. M. Gleeson, M. R. Descour, and E. L. Dereniak,‘‘Portable computed-tomography imaging spectrometer,’’ Proc. SPIE2819, 224–230 ~1996!.R. Glenn Sellar received a BEng degree inengineering physics from McMaster Uni-versity in 1986, a MS degree in space sci-ences from the Florida Institute of Technol-ogy in 1993, and a PhD in optics from theUniversity of Central Florida in 2003. Hehas 16 years of experience in the design,integration, and testing of imaging spec-trometers, for ground-based, airborne, andspaceborne applications. He worked as anoptical engineer on the Wind Imaging Inter-ferometer (WINDII) for the Upper Atmosphere Research Satellite(UARS) and designed the optical system for the Fourier TransformHyperSpectral Imager (FTHSI) on MightySat II. He is member ofSPIE and is currently developing imaging spectrometers for plan-etary science and earth science applications at the Jet PropulsionLaboratory.Glenn D. Boreman received a BS in opticsfrom the Institute of Optics, University ofRochester and a PhD in optics from theOptical Sciences Center, University of Ari-zona. Since 1984, he has been on the fac-ulty of the University of Central Florida,where he is presently Trustee Chair Pro-fessor of Optics. He currently serves as theEditor-in-Chief of Applied Optics, and is apast member of the SPIE Board of Direc-tors. He is co-author of Infrared Detectorsand Systems (Wiley), author of Basic Electro-Optics for ElectricalEngineers, and Modulation Transfer Function in Optical and Electro-Optical Systems, and editor of Infrared Technology 1988–1998 (allSPIE Press). He is a fellow of OSA and SPIE. Along with his stu-dents, he received the 1995 Kingslake Medal from SPIE.Sellar and Boreman: Classification of imaging spectrometers . . .013602-3Optical Engineering January 2005/Vol. 44(1)Downloaded From: https://www.spiedigitallibrary.org/journals/Optical-Engineering on 27 Aug 2019Terms of Use: https://www.spiedigitallibrary.org/terms-of-use

Classification of imaging spectrometers for remote sensing applications

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Classification of imaging spectrometers for remote sensing applications

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