17 June 1996 Nonparametric classification of subpixel materials in multispectral imagery
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Abstract
An effective process for the automatic classification of subpixel materials in multispectral imagery has been developed. The applied analysis spectral analytical process (AASAP) isolates the contribution of specific materials of interest (MOI) within mixed pixels. AASAP consists of a suite of algorithms that perform environmental correction, signature derivation, and subpixel classification. Atmospheric and sun angle correction factors are extracted directly from imagery, allowing signatures produced from a given image to be applied to other images. AASAP signature derivation extracts a component of the pixel spectra that is most common to the training set to produce a signature spectrum and nonparametric feature space. The subpixel classifier applies a background estimation technique to a given pixel under test to produce a residual. A detection occurs when the residual falls within the signature feature space. AASAP was employed to detect stands of Loblolly Pine in a landsat TM scene that contained a variety of species of southern yellow pine. An independent field evaluation indicated that 85% of the detections contained over 20% Loblolly, and that 91% of the known Loblolly stands were detected. For another application, a crop signature derived from a scene in Texas detected occurrences of the same crop in scenes from Kansas and Mexico. AASAP has also been used to locate subpixel occurrences of soil contamination, wetlands species, and lines of communications.
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Eric R. Boudreau, Robert L. Huguenin, Mark A. Karaska, "Nonparametric classification of subpixel materials in multispectral imagery", Proc. SPIE 2758, Algorithms for Multispectral and Hyperspectral Imagery II, (17 June 1996); doi: 10.1117/12.243232; https://doi.org/10.1117/12.243232
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