Translator Disclaimer
11 April 2008 Unsupervised spectral-spatial classification of hyperspectral imagery using real and complex features and generalized histograms
Author Affiliations +
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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
12 PAGES


SHARE
Advertisement
Advertisement
Back to Top