LAUSR

Most spectral mixture analyses in the literature overlook the spatial correlation of neighborhood pixels. The main contribution of this paper is to consider the impacts of both spatial and spectral information prior to endmember (EM) extraction algorithms. Hence, we take advantage of a top-down over-segmentation algorithm in combination with fuzzy c-means (FCM) clustering to identify spatially homogenous over-segments with minimum spectral variability and high spatial correlation. FCM provides a soft segmentation while its partial membership matrix is exploited to calculate a novel local entropy criterion (LEC) at pixels seated in homogenous over-segments. Afterwards, by performing an adaptive threshold per homogenous over-segment, pixels with high LEC values which have high certainty to associate with only one class are selected as pure ones. LEC calculations lead to preserving level of unmixing accuracy while speeding up EM extraction. This subject is important for large images particularly with real-time limitations. With respect to experiments accomplished on synthetic and AVIRIS hyperspectral images, clustering, over-segmentation, and entropy preprocessing has a simple and fast framework while it relatively outperforms the state-of-the-art procedures in terms of extraction accuracy and computing time.