Recently, the autoencoder (AE) has received significant attention in the hyperspectral anomaly detection task. However, all existing AE-based anomaly detectors operate under the linear mixing model, which cannot accurately model the nonlinear mixing phenomenon in practical hyperspectral images (HSIs). Moreover, these AE-based detectors rarely consider the spatial information between pixels, which is crucial to obtain accurate results of anomaly detection. To address the above issues, this paper proposes a transformer-based AE framework (TAEF) for nonlinear hyperspectral anomaly detection. Specifically, the proposed AE framework adopts the transformer as the encoder so that not only the local spatial information, but also the transitive global spatial information can be considered. And the extended multilinear mixing model (EMLM) is embedded into the decoder to accurately characterize the high-order nonlinear mixing phenomenon. By using this transformer-based AE framework, the background of HSIs can be reconstructed effectively. Moreover, a novel method for generating patches is proposed in this paper to support the transformer in the characterization of the transitive global spatial information. Besides, to further improve the accuracy of the background reconstruction, the local-clustering method is adopted to decrease the potential anomalies and increase the sparse backgrounds in the meantime. Finally, the anomalous level of pixel is calculated by the reconstruction error. The experimental results on various real hyperspectral datasets demonstrate that the proposed TAEF outperforms the current state-of-the-art anomaly detectors. In addition, our code is available at: https://github.com/I3ab/TAEF.

Anomaly detection aiming to locate targets distinct from the surrounding background spectra remains a challenging task in hyperspectral applications. The methods based on low- rank decomposition utilize the inherent low-rank characteristic of hyperspectral images (HSIs), which has attracted great interest and achieved many advances in recent years. In order to fully consider the characteristics of HSIs, more appropriate constrains need to be added to the low-rank model. However, there are too many regularizations and mutual constraints between regular- izers, which would result in a reduction in detection accuracy, while an increasing number of trade-off parameters complicates parameter tuning. To address the above problems, we propose a novel method based on merging total variation into low-rank representation (MTVLRR) for hyperspectral anomaly detection in this paper, using a regularizer to reflect the low-rankness and smoothness of the background component of HSIs simultaneously, which can significantly decrease the mutual influence of regularizers and the difficulty of parameter tuning. Experimental results on both simulated and real hyperspectral datasets demonstrate that the proposed MTVLRR has an excellent anomaly detection performance in term of detection accuracy compared with other state-of-the-art methods. In addition, our code is available at: https://github.com/I3ab/MTVLRR.

Recently, autoencoder (AE)-based anomaly detection approaches for hyperspectral images (HSIs) have been extensively proposed; however, the reconstruction accuracy is susceptible to the anomalies and noises. Moreover, these AE-based anomaly detectors simply compress each pixel into a hidden-layer with a lower dimension and then reconstruct it, which does not consider the spatial properties among pixels. To solve the above issues, this paper proposes a background reconstruction framework via a 3D-transformer (3DTR) network for anomaly detection in HSIs. The experimental results on both synthetic and real hyperspectral datasets demonstrate that the proposed 3DTR network is able to effectively detect most of the anomalies by comprehensively considering the spatial correlations among pixels and the spectral similarity among spectral bands of HSIs. In addition, the proposed method exhibits fewer false alarms than both traditional and state-of-the-art (including model-based and AE-based) anomaly detectors owing to the adopted pre-detection procedure and the proposed novel patch-generation method in this paper. Moreover, two ablation experiments adequately verified the effectiveness of each component in the proposed method.

Many linear approaches have been extensively proposed for the anomaly detection problem in hyperspectral images (HSIs), while nonlinear approaches have been rarely studied although most practical cases are nonlinear. Moreover, these existing nonlinear methods simply nonlinearly map each pixel into a high-dimensional space, which does not describe complex light scattering effects between endmembers. To address the above issues, this paper proposes an endmember-kernel-based decomposition model with total variation and sparsity regularizations via union dictionary for the nonlinear anomaly detection in HSIs. The proposed decomposition model utilizes endmember-kernel theory to handle nonlinear interactions between atoms in the dictionary, allowing for effective characterization of complex light scattering effects. By using this endmember-kernel-based decomposition model, a hyperspectral imagery can be decomposed into three components: anomaly, background, and noise. To separate these components effectively, the total variation (TV) and sparsity regularizations are incorporated into the decomposition model to characterize the spatial properties of the background and the anomaly, respectively. Besides, we present a novel construction framework of union dictionary that combines superpixel segmentation and clustering methods sequentially to achieve more accurate dictionary representation capabilities. Finally, the anomalous level of a tested pixel is calculated by the abundances associated with the anomaly dictionary. The experimental results on both synthetic and real hyperspectral data sets demonstrate that the proposed method outperforms several linear and nonlinear state-of-the-art anomaly detectors.