Abstract. The spectral angle mapper (SAM) has been widely used in multispectral and hyperspectral image analysis to measure spectral similarity 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 hyperspectral spectral discrimination measure, which combines the SID and the SAM into a mixed measure. More specifically, let r and rЈ denote two hyperspectral image pixel vectors with their corresponding spectra specified by s and sЈ. Then SAM(s,sЈ) measures the spectral angle between s and sЈ. Similarly, SID(s,sЈ) measures the information divergence between the probability distributions generated by s and sЈ. The proposed new measure, referred to as the SID-SAM mixed measure, can be implemented in two versions, given by SID(s,sЈ)ϫtan(SAM(s,sЈ)) and SID(s,sЈ)ϫsin(SAM(s,sЈ)), where tan and sin are the usual trigonometric functions. The spectral discriminability of such a mixed measure is greatly enhanced by multiplying the spectral abilities of the two measures. In order to demonstrate its utility, a comparative study is conducted 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. [DOI: 10.1117/1.1766301] Subject terms: relative spectral discriminatory entropy (RSDE); relative spectral discriminatory power (RSDPW); relative spectral discriminatory probability (RS-DPB); self-information; spectral angle mapper (SAM); spectral information divergence (SID); spectral information measure (SIM). Paper TPR-030
In REF , Du et al. first applied spectral information divergence on the hyperspectral image classification; the similarity measure is the probability of spectral information divergence distribution between two pixels.
New hyperspectral discrimination measure for spectral characterization
Optical Engineering
Physics