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In this paper, we propose a new nonlinear approach which uses graphs for detecting endmembers in hyperspectral images. Endmembers are defined as the purest points of the image and lie on the boundary of the data cloud. The image is modeled by a graph and in order to reduce the effects of noise and artifacts existent in the image, the superpixel representation is used instead of pixel representation...
In this paper, we propose a new approach based on graphs which can be used for detecting both endmembers and anomalies present in hyperspectral images. After a preliminary oversegmentation of the image using superpixels, the superpixel segment averages are considered as the nodes of graph. A measure of spectral similarity (Euclidean distance) is used as edge weights. Superpixel segmentation is employed...
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