Share Email Print
cover

Proceedings Paper

Spectral feature probabilistic coding for hyperspectral signatures
Author(s): Sumit Chakravarty; Chein-I Chang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Spectral signature coding has been used to characterize spectral features where a binary code book is designed to encode an individual spectral signature and the Hamming distance is then used to perform signature discrimination. The effectiveness of such a binary signature coding largely relies on how well the Hamming distance can capture spectral variations that characterize a signature. Unfortunately, in most cases, such coding does not provide sufficient information for signature analysis, thus it has received little interest in the past. This paper reinvents the wheel by introducing a new concept, referred to as spectral feature probabilistic coding (SFPC) into signature coding. Since the Hamming distance does not take into account the band-to-band variation, it can be considered as a memoryless distance. Therefore, one approach is to extend the Hamming distance to a distance with memory. One such coding technique is the well-known arithmetic coding (AC) which encodes a signature in a probabilistic manner. The values resulting from the AC is then used to measure the distance between two signatures. This paper investigates AC-based signature coding for signature analysis and conducts a comparative analysis with spectral binary coding.

Paper Details

Date Published: 4 May 2006
PDF: 10 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62332C (4 May 2006); doi: 10.1117/12.665281
Show Author Affiliations
Sumit Chakravarty, Univ. of Maryland, Baltimore County (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top