Hyperspectral anomaly detection (HAD) aims to identify anomalous targets that deviate from the surrounding background in unlabeled hyperspectral images (HSIs). Most existing deep networks that exploit reconstruction errors to detect anomalies are prone to fit anomalous pixels, thus yielding small reconstruction errors for anomalies, which is not favorable for separating targets from HSIs. To achieve a superior background reconstruction network for HAD purposes, this article proposes a self-supervised blind-block network (termed BockNet) with a guard window. BockNet creates a blind-block (guard window) in the center of the network’s receptive field, rendering it unable to see the information inside the guard window when reconstructing the central pixel. This process seamlessly embeds a sliding dual-window model into our BockNet, in which the inner window is the guard window and the outer window is the receptive field outside the guard window. Naturally, BockNet utilizes only the outer window information to predict/reconstruct the central pixel of the perceptive field. During the reconstruction of pixels inside anomalous targets of varying sizes, the targets typically fall into the guard window, weakening the contribution of anomalies to the reconstruction results so that those reconstructed pixels converge to the background distribution of the outer window area. Accordingly, the reconstructed HSI can be deemed as a pure background HSI, and the reconstruction error of anomalous pixels will be further enlarged, thus improving the discrimination ability of the BockNet model for anomalies. Extensive experiments on four datasets illustrate the competitive and satisfactory performance of our BockNet compared to other state-of-the-art detectors.

Recent years have witnessed the flourishing of deep learning-based methods in hyperspectral anomaly detection (HAD). However, the lack of available supervision information persists throughout. Additionally, existing unsupervised/semi-supervised learning methods to detect anomalies utilizing reconstruction errors not only generate backgrounds but also reconstruct anomalies to some extent, complicating the identification of anomalies in the original hyperspectral image (HSI). In order to train a network able to reconstruct only background pixels (instead of anomalous pixels), in this paper we propose a new blind-spot self-supervised learning framework (called BS ³ LNet) that generates training patch pairs with blind-spots from a single HSI and trains the network in self-supervised fashion. The BS ³ LNet tends to generate high reconstruction errors for anomalous pixels and low reconstruction errors for background pixels due to the fact that it adopts a blind-spot architecture, i.e., the receptive field of each pixel excludes the pixel itself and the network reconstructs each pixel using its neighbors. The above characterization suits the HAD task well, considering the fact that spectral signatures of anomalous targets are significantly different from those of neighboring pixels. Our network can be considered a superb background generator, which effectively enhances the semantic feature representation of the background distribution and weakens the feature expression for anomalies. Meanwhile, the differences between the original HSI and the background reconstructed by our network are used to measure the degree of anomaly of each pixel, so that anomalous pixels can be effectively separated from the background. Extensive experiments on two synthetic and three real datasets reveal that our BS ³ LNet is competitive with regards to other state-of-the-art approaches.

Most unsupervised or semisupervised hyperspectral anomaly detection (HAD) methods train background reconstruction models in the original spectral domain. However, due to the noise and spatial resolution limitations, there may be a lack of discrimination between backgrounds and anomalies. This makes it easy for the autoencoder to capture the low‐level features shared between the two, thereby increasing the difficulty of separating anomalies from the backgrounds, which runs counter to the purpose of HAD. To this end, the authors map the original spectrums to the fractional Fourier domain (FrFD) and reformulate it as a mapping task in which restoration errors are employed to distinguish background and anomaly. This study proposes a novel frequency‐to‐spectrum mapping generative adversarial network for HAD. Specifically, the depth separable features of backgrounds and anomalies are enhanced in the FrFD. Due to the semisupervised approach, FTSGAN needs to learn the embedded features of the backgrounds, thus mapping and restoring them from the FrFD to the original spectral domain. This strategy effectively prevents the model from focussing on the numerical equivalence of input and output, and restricts the ability of FTSGAN to restore anomalies. The comparison and analysis of the experiments verify that the proposed method is competitive.

Hyperspectral anomaly detection (HAD) aims to locate targets deviating from the background distribution in hyperspectral images (HSIs) without requiring prior knowledge. Most current deep learning-based HAD methods struggle to effectively distinguish anomalies due to limited utilization of supervision information and intrinsic nonlocal self-similarity in HSIs. To this end, this article proposes a novel nonlocal and local feature-coupled self-supervised network (NL2Net) tailored for HAD. NL2Net employs a dual-branch architecture that integrates both local and nonlocal feature extraction. The local feature extraction branch (LFEB) leverages centrally masked and dilated convolutions to extract local spatial-spectral features, while the non-LFEB incorporates a simplified self-attention module to capture long-range dependencies. Furthermore, an improved center block masked convolution strengthens NL2Net ’s focus on surrounding background features, enhancing the background modeling effect. By reconstructing pure backgrounds and suppressing anomalous features, NL2Net achieves precise anomaly separation and superior HAD performance. Experimental results demonstrate its ability to effectively integrate multidimensional features and enhance HAD accuracy, surpassing state-of-the-art methods.

Hyperspectral anomaly detection (HAD) aims to identify anomalous objects that deviate from surrounding backgrounds in an unlabeled hyperspectral image (HSI). Most available neural networks that make use of the reconstruction error to perform HAD tend to fit both backgrounds and anomalies, resulting in small reconstruction errors for both and not being effective in separating targets from background. To address this issue, we develop Dual-window-inspired reconstruction Network (DirectNet), a new background reconstruction network for HAD that seamlessly integrates a sliding dual-window model into a blind-block architecture. Concretely, DirectNet establishes an inner window within the network’s receptive field by erasing the center block information so that the content of the inner window remains invisible during the reconstruction of the central pixel. In addition, the depth of our reconstruction network is adaptive to the size of the input image patch, ensuring that the network’s receptive field aligns with the dimensions of the input patch. The receptive field outside the inner window is considered an outer window. This weakens the impact of anomalies on the reconstruction process, causing the reconstructed pixels to converge toward the background distribution in the outer window region. Consequently, the reconstructed HSI can be regarded as a pure background HSI, leading to further amplification of reconstruction errors for anomalous targets. This enhancement improves the discriminatory ability of DirectNet. Specifically, DirectNet solely utilizes the outer window information to predict/reconstruct the central pixel. As a result, when reconstructing pixels inside anomalous targets of different sizes, the targets primarily fall within the inner window. Comprehensive experiments (conducted on four datasets) demonstrate that DirectNet achieves competitive performance compared to other state-of-the-art detectors.

Recent years have witnessed significant advances of deep learning technology in hyperspectral anomaly detection (HAD). Among these methods, existing unsupervised learning techniques that exploit image reconstruction errors to detect anomalous targets not only generate backgrounds but also reconstruct anomalies to a certain extent, thus failing to recognize anomalies from the original hyperspectral image (HSI). In order to train an efficient background reconstruction network, this paper proposes a new pixel-shuffle down-sampling blind-spot reconstruction framework (called PDBSNet) that operates in a self-supervised fashion. On the one hand, PDBSNet adopts a blind-spot architecture with the receptive field, whose center pixel is set as a blind spot (meaning that the network is unable to see the center pixel), and reconstructs the spectral information of the center pixel using its neighbors. Given that the spectral signatures of anomalous targets are significantly different from those of neighboring pixels, our network tends to generate high reconstruction errors for anomalous pixels and low reconstruction errors for background pixels, and the above characterization of blind-spot architecture is well-suited for the HAD task. On the other hand, we introduce the pixel-shuffle down-sampling (PD) technique, which extends the application range of the blind-spot framework due to the fact that it breaks the spatial correlation between pixels in anomalous targets and improves the discrimination between the anomalous pixels and shuffled neighboring backgrounds. Taken together, our PDBSNet integrates the blind-spot architecture and the PD strategy, effectively enhancing the feature representation of the background and weakening the feature expression of anomalies. It is expected to increase the reconstruction error of anomalous objects, while the differences between the original HSI and the reconstructed one are taken as anomaly scores. Extensive experiments on four datasets reveal that PDBSNet is competitive and remarkable concerning other state-of-the-art detectors.