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Proceedings Paper

Development of a watershed algorithm for multiresolution multidimensional clustering of hyperspectral data
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Paper Abstract

The watershed-clustering algorithm was adapted for use in multi-dimentional spectral space and was used to define clusters in Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. This algorithm identifies clusters as peaks in a B-dimensional topographic relief, where B is the number of wavelength bands. Image pixel spectra are represented as points in this multi-dimensional space. Analysis is done at increasing values of radiometric resolution, defined by the number of segments into which each wavelength axis is divided. Segmentation of the axes divides the multi-dimensional space into bins, and the number pixels in each bin is determined. The histogram of the bin populations defines the topography for the watershed analysis. Spectral clusters correspond to mountains or islands on this multi-dimensional surface. The algorithm is analogous to submerging this topography under water, and revealing clusters by determining when mountain peaks appear as the water surface is lowered. Testing of this algorithm reveals some surprising features. Although increasing the radiometric resolution (bins per axis) generally results in large clusters breaking up into greater numbers of small clusters., this is not always the case. Under some circumstances, the separate clusters can recombine into one large cluster when radiometric resolution is increased. This behavior is caused by the existence of single-pixel voxels, which smooths out the topography, and by the fact that the voxels retain a surprising degree of connectivity, even at high radiometric resolutions. These characteristics of the high-dimensional spectral data provide the basis for further development of the watershed 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: 10.1117/12.478761
Show Author Affiliations
Gerard P. Jellison, Spectral Information Technology Applications Ctr. (United States)
Terrence H. Hemmer, 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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