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- W2022093090 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." @default.
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- W2022093090 date "2002-01-21" @default.
- W2022093090 modified "2023-09-23" @default.
- W2022093090 title "Automated clustering/segmentation of hyperspectral images based on histogram thresholding" @default.
- W2022093090 doi "https://doi.org/10.1117/12.453367" @default.
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