In recent years, many target detection methods for hyperspectral images based on sparse representation (SR) theory have been proposed and achieved good results. However, these methods still have some deficiencies. Specifically, these methods usually give the same weight to the pixels in the neighborhood when they use the spatial information, and obtain the spectral average features to replace the spectral features of the central pixel. However, the influence of each pixel in the neighborhood of the central pixel may be different. In addition, when the dictionary learning method is used for target detection, its performance depends on the constructed dictionary. To solve these problems, an innovative target detection method based on spatial-spectral joint weighted dictionary learning with an online updating mechanism is proposed. First of all, different from traditional SR method, this method integrates the spatial adaptive neighborhood information into the sparse coding stage to realize the spatial-spectral weighted joint SR. Second, the online dictionary updating mechanism and a two-step optimization method are used together to update the dictionary and sparse coding coefficients alternately, which can learn the optimal dictionary set in real time. The experimental results show that the proposed method has better performance when compared with four state-of-the-art dictionary learning-based methods for hyperspectral target detection.

中文翻译：

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基于空间光谱联合加权字典学习和在线更新机制的高光谱目标检测方法

近年来，许多基于稀疏表示（SR）理论的高光谱图像目标检测方法被提出并取得了良好的效果。但是，这些方法仍然存在一些不足。具体而言，这些方法在使用空间信息时通常对邻域内的像素赋予相同的权重，并获得光谱平均特征来代替中心像素的光谱特征。然而，中心像素附近的每个像素的影响可能不同。另外，当字典学习方法用于目标检测时，其性能取决于构建的字典。针对这些问题，提出了一种基于空间谱联合加权字典学习和在线更新机制的创新目标检测方法。首先，与传统的SR方法不同，该方法将空间自适应邻域信息集成到稀疏编码阶段，实现空间谱加权联合SR。其次，结合在线字典更新机制和两步优化方法，交替更新字典和稀疏编码系数，可以实时学习最优字典集。实验结果表明，与四种最先进的基于字典学习的高光谱目标检测方法相比，所提出的方法具有更好的性能。在线字典更新机制和两步优化方法相结合，交替更新字典和稀疏编码系数，实时学习最优字典集。实验结果表明，与四种最先进的基于字典学习的高光谱目标检测方法相比，所提出的方法具有更好的性能。在线字典更新机制和两步优化方法相结合，交替更新字典和稀疏编码系数，实时学习最优字典集。实验结果表明，与四种最先进的基于字典学习的高光谱目标检测方法相比，所提出的方法具有更好的性能。