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On the use of stochastic process-based methods for the analysis of hyperspectral dataFurther development in remote sensing technology requires refinement of information system design aspects, i.e., the ability to specify precisely the data to collect and the means to extract increasing amounts of information from the increasingly rich and complex data stream created. One of the principal directions of advance is that data from much larger numbers of spectral bands can be collected, but with significantly increased signal-to-noise ratio. The theory of stochastic or random processes may be applied to the modeling of second-order variations. A multispectral data set with a large number of spectral bands is analyzed using standard pattern recognition techniques. The data were classified using first a single spectral feature, then two, and continuing on with greater and greater numbers of features. Three different classification schemes are used: a standard maximum likelihood Gaussian scheme; the same approach with the mean values of all classes adjusted to be the same; and the use of a minimum distance to means scheme such that mean differences are used.
Document ID
19930063700
Document Type
Conference Paper
Authors
Landgrebe, David A. (Purdue Univ. West Lafayette, IN, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: IGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vol. 1 (A93-47551 20-43)
Subject Category
EARTH RESOURCES AND REMOTE SENSING
Funding Number(s)
CONTRACT_GRANT: NAGW-925
Distribution Limits
Public
Copyright
Other

Related Records

IDRelationTitle19930063554Analytic PrimaryIGARSS '92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vols. 1 & 2