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Proceedings Paper

Application of multiresolution multidimensional clustering of hyperspectral data using the watershed algorithm
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Paper Abstract

In many applications of remotely-sensed imagery, one of the first steps is partitioning the image into a tractable number of regions. In spectral remote sensing, the goal is often to find regions that are spectrally similar within the region but spectrally distinct from other regions. There is often no requirement that these region be spatially connected. Two goals of this study are to partition a hyperspectral image into groups of spectrally distinct materials, and to partition without human intervention. To this end, this study investigates the use of multi- resolution, multi-dimensional variants of the watershed- clustering algorithm on Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. The watershed algorithm looks for clusters in a histogram: a B-dimensional surface where B is the number of bands used (up to 210 for HYDICE). The algorithm is applied to HYDICE data of the Purdue Agronomy Farm, for which ground truth is available. Watershed results are compared to those obtained by using the commonly-available Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm.

Paper Details

Date Published: 2 August 2002
PDF: 12 pages
Proc. SPIE 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, (2 August 2002); doi: 10.1117/12.478762
Show Author Affiliations
Terrence H. Hemmer, Spectral Information Technology Applications Ctr. (United States)
Gerard P. Jellison, Spectral Information Technology Applications Ctr. (United States)
Darryl G. Wilson, Boeing Co. (United States)


Published in SPIE Proceedings Vol. 4725:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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