Segmentation of hyperspectral images based on histograms of principal components

Jerry Silverman; Stanley R. Rotman; Charlene E. Caefer

doi:10.1117/12.451537

8 November 2002 Segmentation of hyperspectral images from the histograms of principle components

Jerry Silverman, Stanley R. Rotman, Charlene E. Caefer

Proceedings Volume 4816, Imaging Spectrometry VIII; (2002) https://doi.org/10.1117/12.451537
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

Further refinements are presented on a simple and fast way to cluster/segment hyperspectral imagery. In earlier work, it was shown that, starting with the first 2 principal component images, one could form a 2-dimensional histogram and cluster all pixels on the basis of the proximity to the peaks. Issues that we analyzed this year are the proper weighting of the different principal components as a function of the peak shape and automatic methods based on an entropy measure to control the number of clusters and the segmentation of the data to produce the most meaningful results. Examples from both visible and infrared hyperspectral data will be shown.

Citation Download Citation

Jerry Silverman, Stanley R. Rotman, and Charlene E. Caefer "Segmentation of hyperspectral images from the histograms of principle components", Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); https://doi.org/10.1117/12.451537

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