Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms

Julio M. Duarte-Carvajalino; Guillermo Sapiro; Miguel Velez-Reyes

doi:10.1117/12.779142

11 April 2008 Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms

Julio M. Duarte-Carvajalino, Guillermo Sapiro, Miguel Velez-Reyes

Author Affiliations +

Proceedings Volume 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV; 69660F (2008) https://doi.org/10.1117/12.779142
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

In this work, we study unsupervised classification algorithms for hyperspectral images based on band-by-band scalar histograms and vector-valued generalized histograms, obtained by vector quantization. The corresponding histograms are compared by dissimilarity metrics such as the chi-square, Kolmogorov-Smirnorv, and earth mover's distances. The histograms are constructed from homogeneous regions in the images identified by a pre-segmentation algorithm and distance metrics between pixels. We compare the traditional spectral-only segmentation algorithms C-means and ISODATA, versus spectral-spatial segmentation algorithms such as unsupervised ECHO and a novel segmentation algorithm based on scale-space concepts. We also evaluate the use of complex features consisting of the real spectrum and its derivative as the imaginary part. The comparison between the different segmentation algorithms and distance metrics is based on their unsupervised classification accuracy using three real hyperspectral images with known ground truth.

Citation Download Citation

Julio M. Duarte-Carvajalino, Guillermo Sapiro, and Miguel Velez-Reyes "Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660F (11 April 2008); https://doi.org/10.1117/12.779142

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