Hyperspectral anomaly detection is a widely studied topic that has garnered significant attention in recent years. However, designing effective nonlinear detectors remains a challenge for many traditional methods. To address this issue, we propose the integration of a variational autoencoder (VAE) in this paper. The VAE enables efficient feature extraction from hyperspectral images (HSIs) by mapping inputs to latent variables that follow a Gaussian distribution. The resulting latent representations are subsequently passed to the Reed-Xiaoli (RX) detector to obtain the final detection results. Through extensive testing on three real datasets, the detection results demonstrate the superiority of our proposed method.

Blind hyperspectral unmixing (HU) involves identifying pixel spectra as distinct materials (endmembers) and simultaneously determining their proportions (abundances) at each pixel. In this paper, we present Swin-HU, a novel method based on the Swin Transformer, designed to efficiently tackle blind HU. This method addresses the limitations of existing techniques, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViT), in capturing global spatial information and spectral sequence attributes. Swin-HU employs Window Multi-head Self-Attention (W-MSA) and Shifted Window Multi-head Self-Attention (SW-MSA) mechanisms to extract global spatial priors while maintaining linear computational complexity. We evaluate Swin-HU against six other unmixing methods on both synthetic and real datasets, demonstrating its superior performance in endmember extraction and abundance estimation. The source code is available at https://github.com/wangyunjeff/Swin-HU.

The inherent spectral variability in hyperspectral images, the noise, and other factors bring difficulties to traditional detectors to separate the target and background by using linear decision boundaries. In this paper, by generalizing the classical constrained energy minimization (CEM) method, and considering the feature auto-extraction ability of deep neural networks (DNN), we propose a nonlinear detector based on semi-supervised learning (named deepCEM). This approach designs a deep neural network structure to provide a specific form of the nonlinear detector and trains the DNN model with knowledge of target spectra and unlabeled samples. Experiments performed on several hyperspectral data sets show that the proposed method performs better than other state-of-the-art methods.

Spectral unmixing is an effective tool to mine information at the subpixel level from complex hyperspectral images. To consider the spatially correlated materials distributions in the scene, many algorithms unmix the data in a spatial–spectral fashion; however, existing models are usually unable to model spectral variability simultaneously. In this article, we present a variational autoencoder-based deep generative model for spatial–spectral unmixing (DGMSSU) with endmember variability, by linking the generated endmembers to the probability distributions of endmember bundles extracted from the hyperspectral imagery via discriminators. Besides the convolutional autoencoder-like architecture that can only model the spatial information within the regular patch inputs, DGMSSU is able to alternatively choose graph convolutional networks or self-attention mechanism modules to handle the irregular but more flexible data—superpixel. Experimental results on a simulated dataset, as well as two well-known real hyperspectral images, show the superiority of our proposed approach in comparison with other state-of-the-art spatial–spectral unmixing methods. Compared to the conventional unmixing methods that consider the endmember variability, our proposed model generates more accurate endmembers on each subimage by the adversarial training process. The codes of this work will be available athttps://github.com/shuaikaishi/DGMSSUfor the sake of reproducibility.

The complex nature of hyperspectral images makes the analysis of spectral signatures a challenging task in remote sensing. For quantitative analysis, spectral unmixing is a well-established and effective tool to analyze the spectra and spatial distribution of substances in the scene. The classical unmixing algorithms usually fail to tackle spectral variability caused by variations in environmental conditions. Many variants based on the linear mixing process have been proposed to tackle this problem; however, the spectral variability modeling capacity of these algorithms is usually insufficient. In this article, we present a probabilistic generative model to address endmember variability and provide more accurate abundance and endmember estimates. The proposed model simultaneously extracts the endmembers and estimates abundances in an unsupervised manner. In particular, it allows fitting arbitrary endmember distributions through the nonlinear modeling capability of neural networks compared to other methods that use parametric endmember variability models. The performance of the proposed approach is evaluated on both synthetic and real datasets. Experimental results show its superiority in comparison with other state-of-the-art methods. The code of this work is available athttps://github.com/shuaikaishi/PGMSUfor the sake of reproducibility.

Hyperspectral unmixing plays an important role in hyperspectral image processing and analysis. It aims to decompose mixed pixels into pure spectral signatures and their associated abundances. The hyperspectral image contains spatial information in neighborhood regions, and spectral signatures existing in the region also have a high correlation. However, most autoencoder (AE)-based unmixing methods are pixel-to-pixel methods and ignore these priors. It is helpful to add spectral–spatial information into unmixing methods. A recent trend to deal with this problem is to use convolutional neural networks (CNNs). Our proposed framework uses 3-D-CNN-based networks to jointly learn spectral–spatial priors. Moreover, previous AE-based unmixing methods use fixed spectral signatures for each pure material. In our work, we use a carefully designed decoder to cope with the endmember variability issue, and variational inference strategy is applied to add uncertainty property into endmembers. To avoid overfitting, we use structured sparsity regularizers to the encoder networks, and$\ell _{2,1}$-loss is added to the estimated abundances to guarantee the sparseness. Experimental results on both simulated and real data demonstrate the effectiveness of our proposed method.

Spectral signatures are usually affected by variations in environmental conditions. The spectral variability is thus one of the most important and challenging problems to be addressed in hyperspectral unmixing. Generally, it is a non-trivial task to model the endmember variability, and existing spectral unmixing methods that address the spectral variability have different limitations. This paper presents a variational autoencoder (VAE) framework for hyperspectral unmixing accounting for the endmember variability. The endmembers are generated using the posterior distributions of the latent variables to describe their variability in the image. Compared with other existing distribution based methods, the proposed method is able to fit an arbitrary distribution of endmembers for each material through the representation capacity of deep neural networks. Evaluated with both synthetic and real datasets, the proposed method shows superior unmixing results compared with other state-of-the-art unmixing methods.