Analysis of different strategies for incorporating spatial information in the design of endmember extraction algorithms from hyperspectral data

Over the last decade, several algorithms have been developed for automatic or semi-automatic extraction of spectral endmembers from hyperspectral image data. In this paper, we present a thorough analytical comparison of endmember extraction methods which include spatial information in the search of spectral endmembers versus a few algorithms which are exclusively based on spectral information. Our quantitative and comparative assessment of algorithm accuracy and computational performance, conducted using both synthetic and real hyperspectral data, provides interesting findings about the potential benefits that can be obtained after incorporating spatial information into the design of endmember extraction algorithms.