Hyperspectral anomaly detection (HAD) has attracted increasing attention due to its economical and efficient applications. The main challenge lies in the data-starved problem of hyperspectral images (HSIs) and the costliness of manual annotation, making it heavily reliant on the model’s adaptability and robustness to unseen scenes under limited samples. Self-supervised learning offers a solution to this urgency via mining meaningful representations from the data itself. One promising paradigm is leveraging untrained neural networks to reconstruct the background component for revealing anomalous information. Its capability stems from the network architecture and the training process rather than learning from expensive and strongly domain-dependent data, which is naturally applicable to HAD. In this paper, to handle the urgent requirement for self-supervised learning in HAD, we propose a multi-scale network (termed MSNet) that detects anomalies with enhanced separation training. The network architecture consists of several multi-scale convolutional encoder-decoder (CED) layers, considering the spatial characteristics of the anomalies. To suppress the anomalies during background reconstruction, we adopt a new separation training strategy by introducing a soft separator for better practicality on larger datasets. Extensive experiments conducted on 5 commonly used datasets and the HAD100 dataset, demonstrate the superiority of our method over its counterparts. Our code will be available at https://github.com/enter-i-username/MSNet.

Recent research shows that generative adversarial network (GAN) based deep learning derived frameworks can improve the accuracy of hyperspectral image (HSI) classification on limited labeled samples. However, several studies point out that existing GAN-based methods are heavily affected by the complexity and inefficient description issues of HSIs. The discriminator in GAN always attempts to interpret high-dimensional nonlinear spectral knowledge of HSIs, thus resulting in the Hughes phenomenon. Another critical issue is sample generation. The generator is only used as a regularizer for the discriminator, which seriously restricts the performance for classification. In this article, we propose SSAT-GAN, a semisupervised spectral–spatial attention feature extraction approach based on the GAN that feeds raw data into a deep learning framework, in an end-to-end fashion. First, the unlabeled data is added into the discriminator to alleviate the problems of training samples and supplies a reconstructed real HSI data distribution through adversarial training. Second, to enhance the description of HSIs, we build spectral–spatial attention modules (SSAT) and extend them to the discriminator and the generator to extract discriminative characteristics from abundant spatial contexts and spectral signatures. The SSAT modules learn a three-dimensional filter bank with spectral–spatial attention weights to obtain meaningful feature maps to improve the discrimination of the feature representation. In terms of the mode collapse of GANs, the mean minimization loss is employed for unsupervised learning. Experimental results from three real datasets indicate that SSAT-GAN has certain advantages over the state-of-the-art methods.

Hyperspectral anomaly detection aims to separate sparse anomalies from low-rank background components. A variety of detectors have been proposed to identify anomalies, but most of them tend to emphasize characterizing backgrounds with multiple types of prior knowledge and limited information on anomaly components. To tackle these issues, this paper simultaneously focuses on two components and proposes a matrix factorization method with framelet and saliency priors to handle the anomaly detection problem. We first employ a framelet to characterize nonnegative background representation coefficients, as they can jointly maintain sparsity and piecewise smoothness after framelet decomposition. We then exploit saliency prior knowledge to measure each pixel’s potential to be an anomaly. Finally, we incorporate the pure pixel index with Reed-Xiaoli’s method to possess representative dictionary atoms. We solve the optimization problem by using a block successive upper-bound minimization framework with guaranteed convergence. Experiments conducted on benchmark hyperspectral datasets demonstrate that the proposed method outperforms some state-of-the-art anomaly detection methods.

Recently, generative adversarial network (GAN)-based methods for hyperspectral image (HSI) classification have attracted research attention due to their ability to alleviate the challenges brought by having limited labeled samples. However, several studies have demonstrated that existing GAN-based HSI classification methods are limited in redundant spectral knowledge and cannot extract discriminative characteristics, thus affecting classification performance. In addition, GAN-based methods always suffer from the model collapse, which seriously hinders their development. In this study, we proposed a semi-supervised adaptive weighting feature fusion generative adversarial network (AWF²-GAN) to alleviate these problems. We introduced unlabeled data to address the issue of having a small number of samples. First, to build valid spectral–spatial feature engineering, the discriminator learns both the dense global spectrum and neighboring separable spatial context via well-designed extractors. Second, a lightweight adaptive feature weighting component is proposed for feature fusion; it considers four predictive fusion options, that is, adding or concatenating feature maps with similar or adaptive weights. Finally, for the mode collapse, the proposed AWF²-GAN combines supervised central loss and unsupervised mean minimization loss for optimization. Quantitative results on two HSI datasets show that our AWF²-GAN achieves superior performance over state-of-the-art GAN-based methods.

Transformer networks have shown impressive performance for hyperspectral interpretation. Nevertheless, the high-dimensional redundant spectral distribution of hyperspectral images (HSIs) hinders their validity of interaction between features from distant locations. In this letter, we propose the HSI-Mixer, a novel extremely simple convolution neural network (CNN), which is similar in spirit to Transformer to reconsider the remarkable inductive biases of convolutions. In specific, we construct a hybrid measurement-based linear projection (HMLP) to merge spectral signatures and spatial positions of an HSI cuboid. Meanwhile, according to the merging relations between spectral–spatial attributes, we establish both spectral and spatial Mixer blocks to separate features from a mixed volume to a pure one, across either spectral bands or spatial locations, respectively. Furthermore, our HSI-Mixer maintains the same-depth-and-resolution throughout the network. Experimental results on three benchmark datasets demonstrate that our proposal achieves promising performance, in contrast to other state-of-the-art (SOTA) methods. The codes of this work will be available at https://github.com/Blueseatear/IEEE_GRSL_2022_HSI-Mixer .