Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms
Published
Journal Article
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.
Full Text
Duke Authors
Cited Authors
- Duarte-Carvajalino, JM; Sapiro, G; Velez-Reyes, M
Published Date
- June 17, 2008
Published In
Volume / Issue
- 6966 /
International Standard Serial Number (ISSN)
- 0277-786X
Digital Object Identifier (DOI)
- 10.1117/12.779142
Citation Source
- Scopus