DEEP LEARNING-BASED HYPERSPECTRAL TARGET DETECTION WITHOUT EXTRA LABELED DATA

Target detection from hyperspectral images is an important problem. Recently, several deep learning-based target detection algorithms have been proposed. However, most of them require extra well-labeled data to train detectors. In this paper, we propose a deep learning-based target detection algorithm that doesn't require any extra labeled data. The proposed detector is based on the siamese network and the low-rank-sparse autoencoder. The autoencoder separates the test spectrum into a low-rank component and a sparse component, based on the assumption that the normal spectrum space has a low-rank structure while outliers sparsely spread in the image. The low-rank output of the autoencoder and the target spectrum are then separately fed into the Siamese network to get two high level features, and the final cosine similarity score is computed based on two features. To properly train the proposed detector, we develop a data creation method that creates numerous simulative training data. Extensive experiments show that the proposed method achieves state-of-theart results.