Attribute profile based target detection using collaborative and sparse representation

Two hyperspectral target detection methods are introduced in this paper. The proposed methods use the spatial information contained in attribute profiles (APs) in addition to the original spectral information. The first detector is AP based collaborative representation (AP-CR) and the second one is AP based sparse representation (AP-SR). Since the thinning operators extract the details of image, the spatial features extracted by them are used to compose the target subspace. In contrast, since the thickening operators conduct the image details to be similar to the surrounding background, they are used for extraction of spatial features composing the background subspace. The proposed AP-CR and AP-SR methods, by generating two appropriate spectral-spatial subspaces, individually considered for target and background dictionaries, show a superior performance in several popular hyperspectral data from the detection probability and the running time point of views.