Target Detection in Hyperspectral Imagery via Sparse and Dense Hybrid Representation - Guo, Tan (IEEE Xplore, 2021)
Representation-based target detectors for hyperspectral imagery (HSI) have recently aroused a lot of interests. However, existing methods ignore the dictionary structure and cannot guarantee an informative and discriminative representation of test pixels for target detection. To alleviate the problem, this letter proposes a novel sparse and dense hybrid representation-based target detector (SDRD). The proposed detector adopts the idea that the relationship between the background and the target sub-dictionaries is a collaborative competition. The structure of the dictionary is discovered and preserved by learning a sparse and dense hybrid representation for test pixel. Benefitting from this, a compact and discriminative representation can be obtained to better represent the test pixel for an improved detection performance. Experimental results on several HSI data sets verify the effectiveness of SDRD in comparison with several state-of-the-art methods.