Multiple endmembers based unmixing using Archetypal Analysis

Conventional methods for mixed pixel analysis have their limitations in performance when the scenario is highly mixed without pure endmembers or only virtual endmembers can be generated. Moreover, theses approaches do not address the endmember variability. In this study, a multiple endmembers extraction algorithm based on Archetypal Analysis (AA) is proposed to solve the above problems. AA aims at finding distinct patterns in the data and thus, is suitable for endmember extraction. It can also generate vitual pure archetypes when no pure samples exist in the data. Kernel version of AA is investigated for multiple endmember extraction. Informative samples which contribute to the generation of each endmember class can be extracted and used as the multiple endmembers of a single ground cover type. Experimental results show that the multiple endmembers unmixing method using Kernal AA achieves more realistic unmixing results than single endmember based unmixing.