Endmember extraction (EE) is one important step in hyperspectral unmixing. However, some EE methods under pure-pixel assumption may work badly for highly mixed data due to the complexity of image… Click to show full abstract
Endmember extraction (EE) is one important step in hyperspectral unmixing. However, some EE methods under pure-pixel assumption may work badly for highly mixed data due to the complexity of image data. In this work, we propose a linear spectral mixing model-guided artificial bee colony (LSMM-ABC) method for EE to solve the problem under a highly mixed situation. The main innovative point of this work is that each employed bee in LSMM-ABC searches food source position guided by the LSMM, rather than with a neighbor food source position. What is more, this proposed LSMM-ABC is not confined to the pure-pixel assumption. The LSMM could help employed bees to find a better solution in endmember generation based on the ABC algorithm. Experimental results on both synthetic and real Cuprite data sets show us that the proposed LSMM-ABC method can improve the overall EE accuracy compared with the EE methods for highly mixed data.
               
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