In recent years, hyperspectral anomaly detection methods based on representation models have attracted much attention. However, when the dictionary is polluted by anomalous pixels, their performance is greatly affected. To adjust the contributions of different dictionary atoms, traditional methods usually predefine a distance weighting matrix and impose it on the dictionary matrix or coefficient vector, which may not be accurate enough. To solve this problem, a self-paced probabilistic collaborative representation detector (SP-ProCRD) is proposed in this article. It assigns weights for each atom loss term according to the probability that the pixel under test (PUT) belongs to the same class as each dictionary atom. Unlike the predefined weight matrix approach, a self-paced learning (SPL) strategy is used for iterative optimization, so that dictionary atoms participate in the representation from “good” to “bad” ones when solving the model. The representation residuals are utilized to accelerate the convergence. The proposed model can optimally represent each PUT using similar dictionary atoms and minimize the negative impact caused by anomalous atoms contained in the dictionary. In terms of weighting for SPL, an adaptive weighting scheme based on the polynomial self-paced (SP) regularizer is proposed to address the generalization issues of most previous weighting schemes. This scheme improves the generalization and automation of the model. Experimental results reveal that the proposed method produces more accurate results than existing methods and runs efficiently.

中文翻译：

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用于高光谱图像异常检测的自定进度概率协作表示

近年来，基于表示模型的高光谱异常检测方法备受关注。然而，当字典被异常像素污染时，其性能会受到很大影响。为了调整不同字典原子的贡献，传统方法通常预先定义距离权重矩阵并将其施加到字典矩阵或系数向量上，这可能不够准确。为了解决这个问题，本文提出了一种自定进度的概率协作表示检测器（SP-ProCRD）。它根据被测像素（PUT）与每个字典原子属于同一类的概率为每个原子损失项分配权重。与预定义权重矩阵方法不同，自定进度学习（SPL）策略用于迭代优化，以便在求解模型时字典原子参与从“好”到“坏”的表示。利用表示残差来加速收敛。所提出的模型可以使用相似的字典原子来最佳地表示每个 PUT，并最大限度地减少字典中包含的异常原子造成的负面影响。在SPL加权方面，提出了一种基于多项式自步调（SP）正则化器的自适应加权方案，以解决大多数先前加权方案的泛化问题。该方案提高了模型的通用性和自动化程度。实验结果表明，所提出的方法比现有方法产生更准确的结果并且运行高效。