Anomaly detection of hyperspectral data has been gaining particular attention for its ability in detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants can not only extract intrinsic features automatically but also detect anomalies that differ dramatically from others. Many AE-driven algorithms are, thus, proposed for anomaly detection in hyperspectral imagery (HSI), but they suffer from two problems: 1) when there exist anomalies in the training set, AE can generalize so well that it can also learn the abnormal patterns well, thereby reducing the ability to distinguish anomalies from the background and 2) geometric structure among samples are lost in latent space of AE, which is vital in hyperspectral anomaly detection. To tackle these problems, we propose a robust anomaly detector based on the AE framework, named robust graph AE (RGAE) detector, in this article. To be specific, we propose a robust AE framework with $\ell _{2,1}$ -norm that is robust to noise and anomalies during training. Meanwhile, we embed a superpixel segmentation-based graph regularization term (SuperGraph) into AE. This strategy can preserve the geometric structure and the local spatial consistency of HSI simultaneously and also effectively reduce the searching space and execution time for each pixel. Extensive experiments are conducted on five datasets, and the results demonstrate that our method has a better detection performance, after comparing with other state-of-the-art hyperspectral anomaly detectors.

Anomaly detection (AD) has become a hotspot in hyperspectral imagery (HSI) processing due to its advantage in detecting potential targets without prior knowledge, and a variety of algorithms are proposed for a better performance. However, they usually either fail to extract intrinsic features underlying HSIs, or suffer from the contamination of noise and anomalies. To address these problems, we propose a new anomaly detector by integrating fractional Fourier transform (FrFT) with low rank and sparse matrix decomposition (LRaSMD). First, distinctive features of HSI data are extracted via FrFT. Then, row-constrained LRaSMD (RC-LRaSMD), which is more practical and stable than the traditional LRaSMD, is employed to separate background from noise and anomalies. Finally, we implement an atom-selection strategy to construct the background covariance matrix for detection. The experimental results with several HSI data sets demonstrate satisfying detection performance compared with other state-of-the-art detectors.

Hyperspectral target detection is one of the most challenging tasks in remote sensing due to limited spectral information. Many algorithms based on matrix decomposition (MD) are proposed to promote the separation of the background and targets, but they suffer from two problems: (1) Targets are detected with the criterion of reconstruction residuals, and the imbalanced number of background and target atoms in union dictionary may lead to misclassification of targets. (2) The detection results are susceptible to the quality of the $apriori$ target spectra, thus obtaining inferior performance because of the inevitable spectral variability. In this paper, we propose a matrix decomposition-based detector named dictionary learning-cooperated matrix decomposition (DLcMD) for hyperspectral target detection. The procedure of DLcMD is two-fold. First, the low rank and sparse matrix decomposition (LRaSMD) is exploited to separate targets from the background due to its insensitivity to the imbalanced number of background and target atoms, which can reduce the misclassification of targets. Inspired by dictionary learning, the target atoms are updated during LRaSMD to alleviate the impact of spectral variability. After that, a binary hypothesis model specifically designed for LRaSMD is proposed, and a generalized likelihood ratio test (GLRT) is performed to obtain the final detection result. Experimental results on five datasets have shown the reliability of the proposed method. Especially in the Los Angeles-II dataset, the area under the curve (AUC) value is nearly 16% higher than the average value of the other seven detectors, which reveals the superiority of DLcMD in hyperspectral target detection.