Paper
17 June 1996 Spectral shape classification system for Landsat thematic mapper
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Abstract
A multispectral classification system based on an alternative spectral representation is described and its performance over a full landsat thematic mapper (TM) scene evaluated. Spectral classes are represented by their spectral shape -- a vector of binary features that describes the relative values between spectral bands. An algorithm for segmenting or clustering TM data based on this representation is described. After classes have been assigned to a subset of spectral shapes within training areas, the remaining spectral shapes are classified according to their Hamming distance to those that have already been classified. The performance of the spectral shape classifier is compared to a maximum likelihood classifier over five sites that are fairly representative of the full landsat scene considered. Although the performance of the two classifiers is not significantly different within a site, the performance of the spectral shape classifier is significantly better than the maximum likelihood classifier across sites. A full-scene spectral shape classifier is then described which combines spectral signature files that associate classes with spectral shapes derived over the five sites into a single file that is used to classify the full scene. The classification accuracy of the full-scene spectral shape classifier is shown to be superior to that of a stratified maximum-likelihood classifier. The spectral shape classifier is implemented in C and is able to process an entire landsat TM scene in about one hour on a single processor SUN SPARC 10 workstation with 128 megabytes of RAM.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark J. Carlotto "Spectral shape classification system for Landsat thematic mapper", Proc. SPIE 2758, Algorithms for Multispectral and Hyperspectral Imagery II, (17 June 1996); https://doi.org/10.1117/12.243224
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Cited by 3 scholarly publications.
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KEYWORDS
Earth observing sensors

Landsat

Binary data

Classification systems

Image classification

Image segmentation

Scene classification

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