In hyperspectral anomaly detection (HAD), tensor low-rankness is essential for effectively separating background and anomaly. However, most of the current low-rank-based methods do not use the spatial-spectral low-rankness and the nonlocal self-similarity simultaneously. To address this issue, we propose a tensor double nuclear norm-based tensor approximation (TDNN-TA) model with all the priors in a unified convex framework, which can be efficiently handled through a well-organized alternating direction method of multipliers. Especially, to thoroughly model the background by tensor approximation, we propose a tensor double nuclear norm (TDNN), which achieves a more precise and flexible exploration of low-rankness and nonlocal self-similarity by applying different low-rank constraints to the global tensor and the group tensor. Moreover, to explore the intrinsic characteristics of different priors by various tensor ranks, we employ the Fourier transform-based three-directional tensor nuclear norm to approximate the nonlocal group tensor rank, and the framelet-based three-modal tensor nuclear norm to approximate the global tensor rank. Experimental results validated on several real hyperspectral datasets demonstrate that TDNN-TA is effective in detecting different sizes of anomalous targets and achieves competitive results for various scenes.

With the advancement of spectral imaging technology, hyperspectral image (HSI) resources have been rapidly expanding, making cross-dataset HSI classification a critical technique and an inevitable trend for large-scale Earth observation applications. However, most existing approaches transfer from one HSI to another in a supervised way, which limits their ability to leverage multi-source HSI information. Therefore, this paper proposes an unsupervised cross-dataset HSI classification method based on parallel adversarial domain adaptation (PADA), which learns and integrates task-relevant and domain-invariant knowledge from multi-source HSIs to classify a target HSI. Specifically, a source-and-target shared information mining module is designed to mine transferable knowledge from each source HSI. This module employs parallel adversarial learning between a spectral-spatial feature extractor and a task-relevant controller with a domain-invariant discriminator to learn task-relevant and domain-invariant features, thereby mitigating domain shift. Moreover, a source-to-target transferability learning module is proposed to evaluate the cross-dataset transferability of knowledge, which computes domain correlation score to evaluate inter-domain correlation and guide adaptive knowledge transfer, effectively suppressing redundant information and reducing negative transfer caused by low-correlated sources. Finally, a multi-source collaborative classification module is developed to accomplish cross-dataset knowledge transfer, which designs correlation-aware fusion strategy to produce the final classification result by integrating information from each source, ensuring balanced and robust decision-making. Extensive experiments conducted under challenging multi-source cross-dataset settings validate the classification performance and the domain extensibility of the proposed method, demonstrating its potential for large-scale Earth observation applications.

Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation. However, the limited target priors constrain the ability to obtain sufficient features or patterns for background-target discrimination, and spectral variation further exacerbates the difficulty of achieving reliable and robust performance. To address these challenges, this article proposes an efficient self-supervised HTD method with a pyramid state space model (SSM), named HTD-Mamba, which employs spectrally contrastive learning to distinguish between target and background based on the similarity measurement of intrinsic features. Specifically, to obtain sufficient training samples and leverage spatial contextual information, we propose a spatial-encoded spectral augmentation (SESA) technique that encodes all surrounding pixels within a patch into a transformed view of the center pixel. In addition, to explore global band correlations, we divide pixels into continuous group-wise spectral embeddings and introduce Mamba to HTD for the first time to model long-range dependencies of the spectral sequence with linear complexity. Furthermore, to alleviate spectral variation and enhance robust representation, we propose a pyramid SSM as a backbone to capture and fuse multiresolution spectral-wise intrinsic features. Extensive experiments conducted on four public datasets demonstrate that the proposed method outperforms state-of-the-art methods in both quantitative and qualitative evaluations. The code is available athttps://github.com/shendb2022/HTD-Mamba.

Time series forecasting (TSF) has extensive applications in domains, such as energy, traffic, and weather prediction. Currently, existing literature has designed many architectures that combine deep learning models in the frequency domain, and effective results have been achieved. However, handling complex-valued arithmetic poses a challenge for most frequency domain-based models. Additionally, features extracted solely in either the time or frequency domain are not comprehensive enough. To solve these problems, we propose a fully complex network (FCNet) in this work, where all network layers are adapted to handle complex-valued computations to simultaneously learn the information in the real and imaginary parts. First, we utilize time-frequency conversion to obtain time-frequency domain signals. And then we design the time-frequency filter-enhanced block to effectively capture global features from time-frequency signals. Finally, we design the complex-valued time-frequency Transformers Block, which separately extracts information from the time and frequency domains. Experimental evaluations on eight data sets from five benchmark domains demonstrate that our model significantly outperforms state-of-the-art methods in TSF. Code is available athttps://github.com/ZHU-0108/FCNet-main.

Hyperspectral target detection (HTD) is an important issue in earth observation, with applications in both military and civilian domains. However, conventional representation-based detectors are hindered by the reliance on the unknown background dictionary, the limited ability to capture nonlinear representations using the linear mixing model (LMM), and the insufficient background-target recognition based on handcrafted priors. To address these problems, this paper proposes an interpretable representation network that intuitively realizes LMM for HTD, making nonlinear feature expression and physical interpretability compatible. Specifically, a subspace representation network is designed to separate the background and target components, where the background subspace can be adaptively learned. In addition, to further enhance the nonlinear representation and more accurately learn the coefficients, a lightweight multi-scale Transformer is proposed by modeling long-distance feature dependencies between channels. Furthermore, to supplement the depiction for target-background discrimination, a constrained energy minimization (CEM) loss is tailored by minimizing the output background energy and maximizing the target response. The effectiveness of the proposed method is demonstrated on four benchmark datasets, showing its superiority over state-of-the-art methods. The code for this work is available at https://github.com/shendb2022/HTD-IRN for reproducibility purposes.

The problem of target detection in hyperspectral images is an unsupervised binary classification problem with extremely uneven samples. To highlight the target and suppress the background as much as possible, this paper proposes a target detection algorithm based on dual sparse constraints. Specifically, the original image can be decomposed into a background image and a target image. Combined with sparse representation, the target detection problem can be transformed into a problem of optimizing the target and background coefficient matrices. This problem can be solved by the alternating direction method of multipliers. Considering that both the target dictionary and background dictionary are unknown, this paper also proposes a dictionary construction algorithm based on spectral similarity and clustering to obtain relatively complete and pure target and background dictionaries. The experimental results demonstrate that the proposed method outperforms other competing methods in both quantitative performance and visual effect. Code and datasets are available at https://github.com/shendb2022/DSC.

Hyperspectral image (HSI) fusion refers to the reconstruction of a high-resolution HSI by fusing a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI) over the same scene. Recently, researchers have proposed many approaches to handle this issue. However, most of them assume that both the spatial and spectral degradation functions are known, which are often limited or unavailable in reality. This article presents a novel model-driven deep network based on matrix decomposition, which considers spectral correlations and reasonably embeds the well-known observation models. Specifically, the proposed method decomposes the desired HSI into spectral basis and coefficients. The spectral basis can be estimated from the LR-HSI via singular value decomposition. To learn the coefficients, a learning model is constructed by merging the observation models, matrix decomposition, and sparsity into a concise single formulation. For solving the proposed model, a deep framework is built by unrolling the alternating direction method of multipliers (ADMM), dubbed as ADMM-HFNet, where the involved parameters can be learned adaptively. It is worth noting that the spectral basis cannot fully represent the desired HSI. Therefore, another model is constructed here to supplement the approximation error, which can also be embedded in the deep network. After checking on three datasets, it is found that the proposed method stands out from advanced competing techniques in both quality measures and visual effects.