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 discrimantion 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)×tan(SAM(s i,s j)) and SID(s i,s j)×sin(SAM(s i,s j)) where tan(SAM(s i,s j)) and sin(SAM(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.
|Number of pages||10|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - 2003|
|Event||Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX - Orlando, FL, United States|
Duration: 21 Apr 2003 → 24 Apr 2003