An Improved Simplex Maximum Distance Algorithm for Endmember Extraction in Hyperspectral Image

Abstract

Simplex maximum distance (SMD) is an algorithm based on that the pixel with the biggest distance from simplex formed by known endmembers is most likely to be the next endmember. However, SMD involves calculation of some intermediate variables, such as simplex's normal vector, and intersection point of simplex and line, leading to computation complexity. In addition, high brightness points, outliers and isolated noise points in hyperspectral image are often extracted as endmembers in SMD. To overcome these two shortages, an improved simplex maximum distance (ISMD) algorithm is presented in the paper. To simplify computation procedure, ISMD defines the distance from pixel to simplex as ratio of volumes of parallel polyhedrons with adjacent dimensions. Once distances of all pixels from existing simplex are received, a set of similar pixels was selected from multiple pixels with a larger distance according to the spectral angle. The set of pixels is averaged to be the new endmember. The ISMD algorithm was assessed using simulated and real AVIRIS images. Compared with SMD, ISMD better extracted real endmembers in simulated image. And spectral angle between endmember obtained by ISMD and corresponding mineral from USGS spectral library is less for AVIRIS image.