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An interesting target detection framework with transferred deep convolutional neural network (CNN) is proposed. For CNN, many labeled samples are needed to train the multi-layer network. However, for target detection tasks, only few target spectral signatures are available, or they are unknown in anomaly detection. In this work, we employ a reference data and further generate pixel-pairs to enlarge...
In this paper, we propose a joint sparse and collaborative representation-based algorithm for target detection in hyperspectral imagery. The proposed target detection is achieved by the representation of the test samples using a target library and a background library. The sparse representation of given target samples is solved by an ℓ1-norm minimization of the representation weight vector, and the...
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