New hyperspectral discrimination measure for spectral characterization

Abstract

The spectral angle mapper (SAM) has been widely used in multispectral and hyperspectral image analysis to measure spectral simi- larity between substance signatures for material identification. It has been shown that the SAM is essentially the Euclidean distance when the spectral angle is small. Most recently, a stochastic measure, called the spectral information divergence (SID), has been suggested to model the spectrum of a hyperspectral image pixel as a probability distribution, so that spectral variations among spectral bands can be captured more effectively in a stochastic manner. This paper develops a new hyper- spectral spectral discrimination measure, which combines the SID and the SAM into a mixed measure. More specifically, letr and r8 denote two hyperspectral image pixel vectors with their corresponding spectra speci- fied bys and s8. Then SAM(s,s8) measures the spectral angle between s and s8. Similarly, SID(s,s8) measures the information divergence be- tween the probability distributions generated by s and s8. The proposed new measure, referred to as the SID-SAM mixed measure, can be imple- mented in two versions, given by SID(s,s8)3tan(SAM(s,s8)) and SID(s,s8)3sin(SAM(s,s8)), where tan and sin are the usual trigonomet- ric functions. The spectral discriminability of such a mixed measure is greatly enhanced by multiplying the spectral abilities of the two mea- sures. In order to demonstrate its utility, a comparative study is con- ducted among the SID-SAM mixed measure, the SID, and the SAM. Our experimental results have shown that the discriminatory ability of the (SID,SAM) mixed measure can be a significant improvement over the SID and SAM. © 2004 Society of Photo-Optical Instrumentation Engineers.