Abstract. Due to lacking use of prior information, the anomaly detection results are not always satisfactory. However, with the establishment of the spectral library, it becomes possible to obtain one or more spectra of the background in the image to be detected. If we can make use of such background information that is always ignored or discarded, the detection result is very likely to be improved. Hence, we proposed a hyperspectral anomaly detection method using a background endmember signature. To better separate the anomaly from the background, we first perform spectral unmixing to estimate the abundance matrix for further study instead of the original spectral data. In this process, we introduce a non-negative matrix factorization-based unmixing method and a corresponding initialization method using a background endmember. Then the low-rank property contained in the abundance matrix is exploited. A low-rank decomposition method is used to separate the anomalies. The proposed algorithm is evaluated on both synthetic and real data sets. Experiment results show the effectiveness of the proposed method and the improvement brought by the usage of a known background endmember.

中文翻译：

---

使用背景端元特征进行高光谱异常检测

摘要。由于缺乏先验信息的使用，异常检测结果并不总是令人满意。但是，随着光谱库的建立，获得待检测图像中背景的一个或多个光谱成为可能。如果我们能够利用这种总是被忽略或丢弃的背景信息，检测结果很有可能得到改善。因此，我们提出了一种使用背景端元特征的高光谱异常检测方法。为了更好地将异常与背景分开，我们首先进行光谱分离以估计丰度矩阵以供进一步研究而不是原始光谱数据。在这个过程中，我们引入了一种基于非负矩阵分解的解混方法和相应的使用背景端元的初始化方法。然后利用丰度矩阵中包含的低秩属性。低秩分解方法用于分离异常。所提出的算法在合成数据集和真实数据集上进行了评估。实验结果表明了该方法的有效性以及使用已知背景端元带来的改进。