Improved minimum volume simplex analysis algorithm for unmixing of hyperspectral data

Abstract

Some of the robust and high-ability processing methods like the improved Minimum Simplex Volume Analysis (MVSA) algorithm are generally time-consuming in hyperspectral data dealing especially the ground resolution is higher. In this research, MVSA was conducted for spectral analysis of HyMap and Hyperion data after a proposed data reduction to overcome the mentioned problem. The data cloud was therefore clustered based on the spectral angle mapper and Multi-Attribute Decision Making (MADM) based methods were used to determine the best threshold angle. Consequently, four indicators were applied as evaluation attributes for selecting the appropriate alternative, and “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)” and “ELimination Et Choice Translating REality (ELECTRE)” were employed to finalize the decision. The methods resulted in 0.8 and 1.3 degrees as optimal spectral angles for HyMap and Hyperion, respectively. In this case, the image processing computational burden and the noise effect were considerably alleviated and the procedure led to accurate endmember identification.