A NEW CLASSIFICATION-ORIENTED ENDMEMBER EXTRACTION AND SPARSE UNMIXING APPROACH FOR HYPERSPECTRAL DATA

Abundance information has been recently used to assist hyperspectral image classification by combining the information coming from classification and unmixing. The fact that classes are usually inconsistent with endmembers makes it a crucial issue to find possible connections between classification and unmixing. This paper describes a new class-based endmember extraction and sparse unmixing approach aimed at establishing the correspondence between endmembers and classes. The proposed approach is exploited in a semisupervised classification framework that combines classification and unmixing with active learning (AL). During the AL process, the class probabilities and abundance information are exploited simultaneously to select the most informative unlabeled samples for classification purposes. Our approach adopts a well established discriminative probabilistic classifier, the multinomial logistic regression (MLR), to learn the class posterior probabilities. The effectiveness of the proposed method is evaluated using real hyperspectral data set collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer (AVIRIS) over the Indian Pines region, Indiana.