Hyperspectral target detection (HTD) aims to identifying targets within a hyperspectral image (HSI) based on provided target spectra. In the current HTD field, representation-based detectors have attracted much attention. However, there are two prominent challenges that are particularly noteworthy. First, the background class encompasses diverse land covers, making its accurate representation challenging. Second, the detection ability can be significantly influenced by the abnormalities and noise in HSI. To tackle these concerns, we propose a novel background covariance discriminative dictionary learning (BCDDL) model for HTD. To enhance the background representation ability and overcome the sparse noise, we combine the dictionary learning with spectral covariance descriptors and undertake background reconstruction in regional scale. Specifically, the input HSI is pre-processed into superpixels, the spectral covariance of each superpixel is used to provide a compact and flexible description of local regional statistical properties. Further, a novel spatial clustering-based dictionary learning method is proposed to learn the background discriminative covariances dictionary. The collaborative representation model within symmetric positive definite (SPD) manifold is utilized to reconstruct background region and get the residual. By merging the background residual with pixel-wise target reconstruction residual, we derive final detection output. Comprehensive experiments on two public hyperspectral datasets and two novel GaoFen-5 datasets demonstrate the superiority of our BCDDL approach over 10 state-of-the-art methods, especially in terms of suppressing background.

中文翻译：

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用于高光谱目标检测的背景协方差判别字典学习

高光谱目标检测 (HTD) 旨在根据提供的目标光谱识别高光谱图像 (HSI) 内的目标。在当前的HTD领域，基于表示的检测器引起了广泛的关注。然而，有两个突出挑战尤其值得注意。首先，背景类包含不同的土地覆盖，使其准确表示具有挑战性。其次，HSI的异常和噪声会显着影响检测能力。为了解决这些问题，我们提出了一种新的 HTD 背景协方差判别字典学习（BCDDL）模型。为了增强背景表示能力并克服稀疏噪声，我们将字典学习与谱协方差描述符相结合，在区域尺度上进行背景重建。具体来说，输入的HSI被预处理为超像素，每个超像素的光谱协方差用于提供局部区域统计特性的紧凑且灵活的描述。此外，提出了一种新颖的基于空间聚类的字典学习方法来学习背景判别协方差字典。利用对称正定（SPD）流形内的协作表示模型来重建背景区域并获得残差。通过将背景残差与逐像素目标重建残差合并，我们得出最终的检测输出。对两个公共高光谱数据集和两个新颖的 GauFen-5 数据集的综合实验证明了我们的 BCDDL 方法相对于 10 种最先进的方法的优越性，特别是在抑制背景方面。