Hyperspectral sparse unmixing based on multiple dictionary pruning

Abstract

ABSTRACT When a spectral library is known, hyperspectral sparse unmixing could obtain the abundance images, which estimate the fractional proportions in each pixel in a hyperspectral image scene. Dictionary pruning (DP) methods furtherly improve the performance of the hyperspectral sparse unmixing by reducing the spectral dictionary to a smaller subset. However, the current hyperspectral sparse unmixing algorithms usually only use a single DP method to obtain the subset, which affects the sparse unmixing results. In this study, we propose a multiple dictionary pruning (MDP) method to improve the performance of hyperspectral sparse unmixing algorithms, making them more accurate. MDP consists of three DP methods, namely, spectral angle mapping (SAM), spectral information divergence (SID) and robust multiple signal classification (RMUSIC). In MDP, we first make a difference between SAM and SID to produce the first level subset named as SS, reducing the impact of non-target information spectrums on the accuracy of sparse unmixing. We then use the RMUSIC to handle the SS to obtain the second level subset named as SSTR, reducing the endmember variability and calibration errors. Finally, we introduce the more accurate subset SSTR into the current hyperspectral sparse unmixing algorithms to produce the sparse unmixing results. Experimental results on two simulated data sets and one real data set show that the proposed MDP method is superior to the traditional DP methods.