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Spectral angle mapper (SAM) has been widely used as a spectral similarity measure for multispectral and hyperspectral image analysis. It has been shown to be equivalent to Euclidean distance when the spectral angle is relatively small. Most recently, a stochastic measure, called spectral information divergence (SID) has been introduced to model the spectrum of a hyperspectral image pixel as a probability distribution so that spectral variations can be captured more effectively in a stochastic manner. This paper develops a new hyperspectral spectral discriminant measure, which is a mixture of SID and SAM. More specifically, let x i and x j denote two hyperspectral image pixel vectors with their corresponding spectra specified by s i and s j . SAM is the spectral angle of x i and x j and is defined by SAM(s i ,s j ). Similarly, SID measures the information divergence between x i and x j and is defined by SID(s i ,s j ). The new measure, referred to as (SID,SAM)-mixed measure has two variations defined by SID(s i ,s j )xtan(SAM(s i ,s j ) and SID(s i ,s j )xsinSAM(s i ,s j ) where tan SAM(s i ,s j ) and sinSAM(s i ,s j ) are the tangent and the sine of the angle between vectors x and y. The advantage of the developed (SID,SAM)-mixed measure combines both strengths of SID and SAM in spectral discriminability. In order to demonstrate its utility, a comparative study is conducted among the new measure, SID and SAM where the discriminatory power of the (SID,SAM)-mixed measure is significantly improved over SID and SAM.
Proceedings of SPIE – SPIE
Published: Sep 24, 2003
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