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.

中文翻译：

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通过稀疏和密集混合表示的高光谱图像中的目标检测

用于高光谱图像 (HSI) 的基于表示的目标检测器最近引起了很多兴趣。然而，现有方法忽略了字典结构并且不能保证用于目标检测的测试像素的信息性和判别性表示。为了缓解这个问题，这封信提出了一种新颖的基于稀疏和密集混合表示的目标检测器（SDRD）。所提出的检测器采用了背景和目标子词典之间的关系是协作竞争的思想。通过学习测试像素的稀疏和密集混合表示来发现和保存字典的结构。受益于此，可以获得紧凑且有区别的表示以更好地表示测试像素以提高检测性能。