Hyperspectral image classification using spectral histograms and semi-supervised learning

Sol M. Cruz Rivera; Vidya Manian

doi:10.1117/12.778222

11 April 2008 Hyperspectral image classification using spectral histograms and semi-supervised learning

Sol M. Cruz Rivera, Vidya Manian

Proceedings Volume 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV; 69660G (2008) https://doi.org/10.1117/12.778222
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

In this paper, an algorithm that extracts regional texture information by computing spectral difference histograms over window extents in hyperspectral images is presented. The spectral angle distance is used as the spectral metric and different window sizes are explored for computing the histogram. The histograms are used in a semi-supervised learning framework that uses both labeled and unlabeled samples for training the support vector machine classifier, which is then tested with unlabeled samples. Results are presented with real and synthetic hyperspectral images. The method performs well with high spatial resolution images. The algorithm performs well under different noise levels.

Citation Download Citation

Sol M. Cruz Rivera and Vidya Manian "Hyperspectral image classification using spectral histograms and semi-supervised learning", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660G (11 April 2008); https://doi.org/10.1117/12.778222

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