ABSTRACT

In the acquisition process of hyperspectral images (HSIs), each band may be contaminated with different degrees of mixing noise. For hyperspectral anomaly detection (HAD) tasks, bands with higher noise contamination levels provide more interference information, thus affecting the detection results. In order to reduce the negative effect of noise in HSIs and improve the robustness of the detector, we propose a self-paced collaborative representation with manifold weighting hyperspectral anomaly detector (SPCRMW). Each band is given an order to join the collaborative representation model according to the degree of noise contamination. Moreover, a novel manifold learning reconstruction-based regularization matrix is proposed to reduce the effect of potential anomalous pixels mixed in the background on collaborative representations. It can automatically assign weights to the background pixels by manifold learning reconstruction error. The results compared with six state-of-the-art HAD methods on three real hyperspectral datasets are presented and illustrate the superiority of the proposed SPCRMW method.

摘要

在高光谱图像（HSI）的采集过程中，每个波段都可能受到不同程度的混合噪声的污染。对于高光谱异常检测（HAD）任务，具有较高噪声污染水平的频段会提供更多的干扰信息，从而影响检测结果。为了减少 HSI 中噪声的负面影响并提高检测器的鲁棒性，我们提出了一种具有多种加权高光谱异常检测器 (SPCRMW) 的自定进度协同表示。根据噪声污染程度，给每个频段一个加入协同表示模型的顺序。此外，提出了一种新颖的基于流形学习重建的正则化矩阵，以减少背景中混合的潜在异常像素对协作表示的影响。它可以通过流形学习重建误差自动为背景像素分配权重。给出了在三个真实高光谱数据集上与六种最先进的 HAD 方法进行比较的结果，并说明了所提出的 SPCRMW 方法的优越性。