Automated clustering/segmentation of hyperspectral images based on histogram thresholding

Jerry Silverman; Charlene E. Caefer; Jonathan Martin Mooney; Melanie M. Weeks; Pearl Yip

doi:10.1117/12.453367

17 January 2002 Automated clustering/segmentation of hyperspectral images based on histogram thresholding

Jerry Silverman, Charlene E. Caefer, Jonathan Martin Mooney, Melanie M. Weeks, Pearl Yip

Proceedings Volume 4480, Imaging Spectrometry VII; (2002) https://doi.org/10.1117/12.453367
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

Abstract

A very simple and fast technique for clustering/segmenting hyperspectral images is described. The technique is based on the histogram of divergence images; namely, single image reductions of the hyperspectral data cube whose values reflect spectral differences. Multi-value thresholds are set from the local extrema of such a histogram. Two methods are identified for combining the information of a pair of divergence images: a dual method of combining thresholds generated from 1D histograms; and a true 2D histogram method. These histogram-based segmentations have a built-in fine to coarse clustering depending on the extent of smoothing of the histogram before determining the extrema. The technique is useful at the fine scale as a powerful single image display summary of a data cube or at the coarser scales as a quick unsupervised classification or a good starting point for an operator-controlled supervised classification. Results will be shown for visible, SWIR, and MWIR hyperspectral imagery.

Citation Download Citation

Jerry Silverman, Charlene E. Caefer, Jonathan Martin Mooney, Melanie M. Weeks, and Pearl Yip "Automated clustering/segmentation of hyperspectral images based on histogram thresholding", Proc. SPIE 4480, Imaging Spectrometry VII, (17 January 2002); https://doi.org/10.1117/12.453367

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