## Abstract

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.

Original language | English |
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Pages (from-to) | 430-439 |

Number of pages | 10 |

Journal | Proceedings of SPIE - The International Society for Optical Engineering |

Volume | 5093 |

DOIs | |

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 |