TRANSFERRED DEEP LEARNING FOR HYPERSPECTRAL TARGET DETECTION

An interesting target detection framework with transferred deep convolutional neural network (CNN) is proposed. For CNN, many labeled samples are needed to train the multilayer 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 the sample size. A multi-layer CNN is trained by using difference between pixel-pairs generated from the reference image scene. During testing, there are two cases: (1) for anomaly detection, difference between pixel-pairs, constructed by combing the center pixel and its surrounding pixels, is classified by the trained CNN with result of similarity measurement; and (2) for supervised target detection, difference between pixel-pairs, constructed by combing the testing pixel and the known spectral signatures, is classified. The detection output is simply generated by averaging these similarity scores. Experimental performance demonstrates that the proposed strategy outperforms the classic detectors.