Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T21:11:33.793Z Has data issue: false hasContentIssue false

14 - Hyper- and Multispectral Visible and Near-Infrared Imaging Analysis

from Part III - Analysis Methods

Published online by Cambridge University Press:  15 November 2019

By
William H. Farrand ,
Erzsébet Merényi  and
Mario C. Parente
Edited by
Janice L. Bishop ,
James F. Bell III  and
Jeffrey E. Moersch
Janice L. Bishop
Affiliation:
SETI Institute, California
James F. Bell III
Affiliation:
Arizona State University
Jeffrey E. Moersch
Affiliation:
University of Tennessee, Knoxville
Get access

Summary

Multi- and hyperspectral sensors in the visible to short-wave infrared (0.4–2.5 μm) are sensitive to spectral features caused by electronic charge transfer and transition metal crystal field band as well as molecular overtone absorptions. This chapter reviews several processing techniques used to map materials on planetary surfaces based on their reflectance spectra in this spectral region. Techniques that are reviewed include spectral matching in the form of spectral angle and spectral information divergence, linear and nonlinear spectral unmixing, partial unmixing/matched filters, and machine learning approaches in the form of self-organizing maps, neural network classification, and support vector machines.

Keywords

mineralogyspectroscopyhyperspectralmultispectralspectral unmixingmachine learningmatched filterspectral end-membersintrinsic dimensionalityPancam
Type
Chapter
Information
Remote Compositional Analysis
Techniques for Understanding Spectroscopy, Mineralogy, and Geochemistry of Planetary Surfaces
 , pp. 307 - 323
Publisher: Cambridge University Press
Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, J.B., Smith, M.O., & Gillespie, A.R. (1993) Imaging spectroscopy: Interpretation based on spectral mixture analysis. In: Remote geochemical analysis: Elemental and mineralogical composition (Pieters, C.M. & Englert, , eds.). Cambridge University Press, New York, 145–166.Google Scholar
Boardman, J.W. (1993) Automating spectral unmixing of AVIRIS data using convex geometry concepts. 4th JPL Airborne Earth Science Workshop, 11–14.Google Scholar
Boardman, J.W. & Kruse, F.A. (1994) Automated spectral analysis: A geologic example using AVIRIS data, north Grapevine Mountains, Nevada. Proceedings of the 10th Thematic Conference on Geological Remote Sensing, ERIM, Ann Arbor, MI, I-407–418.Google Scholar
Boardman, J.W., Kruse, F.A., & Green, R.O. (1995) Mapping target signatures via partial unmixing of AVIRIS data. 5th Annual JPL Airborne Earth Science Workshop.Google Scholar
Boser, B.E., Guyon, I.M., & Vapnik, V.N. (1992) A training algorithm for optimal margin classifiers. Proceedings of the Annual Workshop on Computational Learning Theory, 144–152.CrossRefGoogle Scholar
Brown, M., Lewis, H.G., & Gunn, S.R. (2000) Linear spectral mixture models and support vector machines for remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 38, 2346–2360.Google Scholar
Bruzzone, L., Chi, M., & Marconcini, M. (2006) A novel transductive SVM for semisupervised classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44, 3363–3373.CrossRefGoogle Scholar
Camps-Valls, G. & Bruzzone, L. (2005) Kernel-based methods for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 43, 1351–1362.CrossRefGoogle Scholar
Camps-Valls, G., Gomez-Chova, L., Muñoz-Marí, J., Vila-Francs, J., & Calpe-Maravilla, J. (2006) Composite kernels for hyperspectral image classification. IEEE Geoscience Remote Sensing Letters, 3, 93–97.CrossRefGoogle Scholar
Camps-Valls, G., Shervashidze, N., & Borgwardt, K.M. (2010) Spatio-spectral remote sensing image classification with graph kernels. IEEE Geoscience Remote Sensing Letters, 7, 741–745.CrossRefGoogle Scholar
Chandrasekhar, S. (1960) Radiative transfer. Dover Publications, Mineola, NY.Google Scholar
Chang, C.-I. (2000) An information theoretic-based approach to spectral variability, similarity and discriminability for hyperspectral image analysis. IEEE Transactions on Information Theory, 46, 1927–1932.Google Scholar
Chen, J.Y. & Reed, I.S. (1987) A detection algorithm for optical targets in clutter. IEEE Transactions on Aerospace Electronic Systems, AES-23(1).Google Scholar
Combe, J.P., Le Mouelic, S., Sotin, C., et al. (2008) Analysis of OMEGA/Mars express data hyperspectral data using a multiple-endmember linear spectral unmixing model (MELSUM): Methodology and first results. Planetary and Space Science, 56, 951–975.Google Scholar
Cortes, C. & Vapnik, V.N. (1995) Support-vector networks. Machine Learning, 20(3), 273–297.CrossRefGoogle Scholar
Farrand, W.H. & Harsanyi, J.C. (1995) Discrimination of poorly exposed lithologies in imaging spectrometer data. Journal of Geophysical Research, 100, 1565–1578.CrossRefGoogle Scholar
Farrand, W.H. & Harsanyi, J.C. (1997) Mapping the distribution of mine tailings in the Coeur d’Alene River Valley, Idaho through the use of a Constrained Energy Minimization technique. Remote Sensing of the Environment, 59, 64–76.Google Scholar
Farrand, W.H., Bell, J.F. III, Johnson, J.R., et al. (2008a) Rock spectral classes observed by the Spirit rover’s Pancam on the Gusev crater plains and in the Columbia Hills. Journal of Geophysical Research, 113, E12S38, DOI:10.1029/2008JE003237.Google Scholar
Farrand, W.H., Merényi, E., Johnson, J.R., & Bell, J.F., III (2008b) Comprehensive mapping of spectral classes in the imager for Mars Pathfinder Super Pan. Mars, 4, 33–55.CrossRefGoogle Scholar
Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., & Tilton, J.C. (2013) Advances in spectral-spatial classification of hyperspectral images. Proceedings of the IEEE, 101, 652–675.CrossRefGoogle Scholar
Felder, M.P., Grumpe, A., & Wöhler, C. (2014) Automatic segmentation of spectrally similar lunar surface areas with emphasis on the spectral absorption features. 45th Lunar Planet. Sci. Conf., Abstract # 2537.Google Scholar
Friedman, J.H. & Tukey, J.W. (1974) A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers, 23, 881–890.CrossRefGoogle Scholar
Gilmore, M.S., Bornstein, B., Merrill, M.D., Castaño, R., & Greenwood, J.P. (2008) Generation and performance of automated jarosite mineral detectors for visible/near-infrared spectrometers at Mars. Icarus, 195, 169–183.Google Scholar
Gilmore, M.S., Thompson, D.R., Anderson, L.J., Karamzadeh, N., Mandrake, L., & Castaño, R. (2011) Superpixel segmentation for analysis of hyperspectral datasets, with application to CRISM data, M3 data, and Ariadnes Chaos, Mars. Journal of Geophysical Research, 116, E07001, DOI:10.1029/2010JE003763.CrossRefGoogle Scholar
Gomez-Chova, L., Camps-Valls, G., Muñoz-Marí, J., & Calpe, J. (2008) Semi-supervised image classification with Laplacian support vector machines. IEEE Geoscience Remote Sensing Letters, 5, 336–340.CrossRefGoogle Scholar
Green, A.A., Berman, M., Switzer, P., & Craig, M.D. (1988) A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on Geoscience and Remote Sensing, 26, 65–74.CrossRefGoogle Scholar
Gruninger, J.H., Ratkowski, A.J., & Hoke, M.L. (2004) The sequential maximum angle convex cone (SMACC) endmember model. Proceedings of SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, 1, DOI:10.1117/12.543794.Google Scholar
Gualtieri, J.A. & Chettri, S. (2000) Support vector machines for classification of hyperspectral data. IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium, IEEE 2000 International Support vector machines for classification of hyperspectral data, 2, 813–815.Google Scholar
Hapke, B. (1981) Bidirectional reflectance spectroscopy: 1. Theory. Journal of Geophysical Research, 86, 3039–3054.CrossRefGoogle Scholar
Hapke, B. (2012) Theory of reflectance and emittance spectroscopy. Cambridge University Press, New York.CrossRefGoogle Scholar
Harsanyi, J.C. (1993) Detection and classification of subpixel spectral signatures in hyperspectral image sequences. PhD dissertation, Department of Electrical Engineering, University of Maryland.Google Scholar
Hogan, R.C. & Roush, T.L. (2002) SOM classification of martian TES data. 23rd Lunar Planet. Sci. Conf., Abstract #1693.Google Scholar
Howell, E.S., Merényi, E., & Lebofsky, L.A. (1994) Using neural networks to classify asteroid spectra. Journal of Geophysical Research, 99, 10,847–10,865.Google Scholar
Kohonen, T. (1988) Self-organization and associative memory. Springer-Verlag, New York.CrossRefGoogle Scholar
Kruse, F.A., Lefkoff, A.B., Boardman, J.W., et al. (1993) The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44, 145–163.CrossRefGoogle Scholar
Kullback, S. (1968) Information theory and statistics. Dover, Gloucester, MA.Google Scholar
Li, J., Marpu, P.R., Plaza, A., Bioucas-Dias, J., & Benediktsson, J.A. (2013) Generalized composite kernel framework for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 51, 4816–4829.Google Scholar
Liu, Y., Glotch, T.D., Scudder, N., et al. (2016) End-member identification and spectral mixture analysis of CRISM hyperspectral data: A case study on southwest Melas Chasma, Mars. Journal of Geophysical Research, 121, 2004–2036.CrossRefGoogle Scholar
Maulik, U. & Chakraborty, D. (2017) Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques. IEEE Geoscience and Remote Sensing Magazine, 5(1), 33–52.CrossRefGoogle Scholar
Melgani, F. & Bruzzone, L. (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, 778–1790.Google Scholar
Merényi, E., Farrand, W.H., Brown, R.H., Villmann, Th., & Fyfe, C. (2007) Information extraction and knowledge discovery from high-dimensional and high-volume complex data sets through precision manifold learning. Proceedings of NASA Science Technology Conference (NSTC2007), College Park, MD, June 19–21, 2007.Google Scholar
Merényi, E., Taşdemir, K., & Zhang, L. (2009) Learning highly structured manifolds: Harnessing the power of SOMs. Similarity based clustering. (Biehl, M., Hammer, B., Verleysen, M., & Villmann, T., eds.). Lecture Notes in Computer Science. Springer, Berlin and Heidelberg, 138–168.CrossRefGoogle Scholar
Merényi, E., Farrand, W.H., Taranik, J.V., & Minor, T.B. (2014) Classification of hyperspectral imagery with neural networks: Comparison to conventional tools, EURASIP Journal on Advances in Signal Processing, 71, DOI:10.1186/1687-6180-2014-71.Google Scholar
Merényi, E., Taylor, J., & Isella, A. (2016) Mining complex hyperspectral ALMA cubes for structure with neural machine learning. Proceedings of the IEEE Symposium Series of Computational Intelligence and Data Mining, SSCI 2016, Athens, Greece, December 6–9, 2016, DOI:10.1109/SSCI.2016.7849952.CrossRefGoogle Scholar
Moser, G., Serpico, S.B., & Benediktsson, J.A. (2013) Land-cover mapping by Markov modeling of spatial-contextual information. Proceedings of the IEEE, 101, 631–651.CrossRefGoogle Scholar
Poulet, F. & Erard, S. (2004) Nonlinear spectral mixing: Quantitative analysis of laboratory mineral mixtures. Journal of Geophysical Research, 109(E2), DOI:10.1029/2003JE002179.CrossRefGoogle Scholar
Poulet, F., Cuzzi, J.N., Cruikshank, D.P., Roush, T., & Dalle Ore, C.M. (2002) Comparison between the Shkuratov and Hapke scattering theories for solid planetary surfaces: Application to the surface composition of two Centaurs. Icarus, 160, 313–324.CrossRefGoogle Scholar
Poulet, F., Bibring, J.-P., Langevin, Y., et al. (2009) Quantitative compositional analysis of martian mafic regions using MEx/OMEGA reflectance data: 1. Methodology, uncertainties and examples of application. Icarus, 201, 69–83.CrossRefGoogle Scholar
Ramsey, M.S. & Christensen, P.R. (1998) Mineral abundance determination: Quantitative deconvolution of thermal emission spectra, Journal of Geophysical Research, 103, 577–596.CrossRefGoogle Scholar
Reed, I.S. & Yu, X. (1990) Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38, 1760–1770.Google Scholar
Ren, H. & Chang, C.I. (2000) A target-constrained interference-minimized filter for subpixel target detection in hyperspectral imagery. IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. IEEE 2000 International Support vector machines for classification of hyperspectral data, 4. 1545–1547.Google Scholar
Richards, J.A. (2013) Supervised classification techniques. In: Remote sensing digital image analysis. Springer, Berlin and Heidelberg, 247–318.Google Scholar
Roush, T.L. & Hogan, R.C. (2007) Automated classification of visible and near-infrared spectra using self-organizing maps. Proceedings of the IEEE Aerospace Conference 2007, 1–10, DOI:10.1109/AERO.2007.352701.CrossRefGoogle Scholar
Roush, T.L., Helbert, J., Hogan, R.C., & Maturilli, A. (2007) Classification of Mars analogue mixtures and end-member minerals using self-organizing maps. 38th Lunar Planet. Sci. Conf., Abstract #1291.Google Scholar
Schaum, A.P. (2001) Spectral subspace matched filtering. Proceedings of the SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, 1 (August 20, 2001), DOI:10.1117/12.436996.Google Scholar
Schowengerdt, R.A. (2012) Techniques for image processing and classifications in remote sensing. Academic Press, San Diego.Google Scholar
Shkuratov, Y.G., Starukhina, L., Hoffmann, H., & Arnold, G. (1999) A model of spectral albedo of particulate surfaces: Implications for optical properties of the Moon, Icarus, 137, 235–246.Google Scholar
Singer, R.B. & McCord, T.B. (1979) Mars: Large scale mixing of bright and dark materials and implications for analysis of spectral bright regions on Mars. Proceedings of the 10th Lunar Planet. Sci. Conf., 1825–1848.Google Scholar
Sklute, E.C., Glotch, T.D., Piatek, J., Woerner, W., Martone, A., & Kraner, M. (2015) Optical constants of synthetic potassium, sodium, and hydronium jarosite. American Minerologist, 100, 1110–1122.CrossRefGoogle Scholar
Smith, M.O., Roberts, D.A., Hill, J., et al. (1994) A new approach to quantifying abundances of materials in multispectral images. Geoscience and Remote Sensing Symposium, 1994. IGARSS’94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation, International, 2372–2374.CrossRefGoogle Scholar
Stocker, A., Reed, I.S., & Yu, X. (1990) Multidimensional signal processing for electro-optical target detection. Proceedings of SPIE 1305, Signal and Data Processing of Small Targets 1990, 218, DOI:10.1117/12.21593.Google Scholar
Taşdemir, K. & Merényi, E. (2008) Cluster analysis in remote sensing spectral imagery through graph representation and advanced SOM representation. 11th International Conference on Discovery Science, DS-2008, Budapest, Hungary, October 13–16, 2008. Lecture Notes in Computer Science, Volume 5255/2008, 272–283.CrossRefGoogle Scholar
Tuia, D. & Camps-Valls, G. (2009) Semi-supervised remote sensing image classification with cluster kernels. IEEE Geoscience and Remote Sensing Letters, 6, 224–228.CrossRefGoogle Scholar
Tuia, D., Camps-Valls, G., Matasci, G., & Kanevski, M. (2010) Learning relevant image features with multiple kernel classification. IEEE Transactions on Geoscience and Remote Sensing, 48, 3780–3791.CrossRefGoogle Scholar
Tuia, D., Volpi, M., Copa, L., Kanevski, M., & Muñoz-Marí, J. (2011) A survey of active learning algorithms for supervised remote sensing image classification. IEEE Journal of Selected Topics on Signal Processing, 5, 606–617.CrossRefGoogle Scholar
Varshney, P.K. & Arora, M.K. (2004) Advanced image processing techniques for remotely sensed hyperspectral data. Springer Science+Business Media, New York.CrossRefGoogle Scholar
Vieira, E.F. & Ponz, J.D (1998) Automated spectral classification using astronomical data analysis software and systems VII. A.S.P. Conference Series, 145, 508.Google Scholar
Winter, M.E. (1999) N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data. SPIE’s International Symposium on Optical Science, Engineering, and Instrumentation. International Society for Optics and Photonics, 266–275.Google Scholar
Zhang, L., Merényi, E., Grundy, W.M., & Young, E.Y. (2010) Inference of surface parameters from near-infrared spectra of crystalline H2O ice with neural learning. Publications of the Astronomical Society of the Pacific, 122 (893), 839–852.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×