The accuracy of anomaly detection in hyperspectral images (HSIs) faces great challenges due to the high dimensionality, redundancy of data, and correlation of spectral bands. In this paper, to further improve the detection accuracy, we propose a novel anomaly detection method based on texture feature extraction and a graph dictionary-based low rank decomposition (LRD). First, instead of using traditional clustering methods for the dictionary, the proposed method employs the graph theory and designs a graph Laplacian matrix-based dictionary for LRD. The robust information of the background matrix in the LRD model is retained, and both the low rank matrix and the sparse matrix are well separated while preserving the correlation of background pixels. To further improve the detection performance, we explore and extract texture features from HSIs and integrate with the low-rank model to obtain the sparse components by decomposition. The detection results from feature maps are generated in order to suppress background components similar to anomalies in the sparse matrix and increase the strength of real anomalies. Experiments were run on one synthetic dataset and three real datasets to evaluate the performance. The results show that the performance of the proposed method yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), i.e., 0.9845, 0.9962, 0.9699, and 0.9900 for different datasets, respectively. Compared with seven other state-of-the-art algorithms, our method yielded the highest average AUC for ROC in all datasets.

中文翻译：

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通过基于图字典的低秩分解和纹理特征提取进行高光谱异常检测

由于高维，数据冗余以及光谱带的相关性，高光谱图像（HSI）中异常检测的准确性面临着巨大的挑战。为了进一步提高检测精度，我们提出了一种基于纹理特征提取和基于图字典的低秩分解（LRD）的异常检测方法。首先，该方法采用图论，设计了基于图拉普拉斯矩阵的LRD字典，而不是使用传统的字典聚类方法。保留了LRD模型中背景矩阵的鲁棒信息，并且在保留背景像素的相关性的同时，低秩矩阵和稀疏矩阵都很好地分离了。为了进一步提高检测性能，我们探索和提取HSI的纹理特征，并与低秩模型集成，以通过分解获得稀疏分量。生成来自特征图的检测结果是为了抑制类似于稀疏矩阵中异常的背景成分，并增加真实异常的强度。在一个合成数据集和三个真实数据集上进行了实验，以评估性能。结果表明，该方法的性能在接收器工作特性（ROC）的曲线下平均面积（AUC）方面具有竞争性结果，即对于不同的数据集，分别为0.9845、0.9962、0.9699和0.9900。与其他七个最新算法相比，我们的方法在所有数据集中产生的ROC的平均AUC最高。