The rapid expansion of low earth orbit (LEO) satellite networks exposes critical limitations in conventional resource allocation schemes, which are unable to simultaneously optimize spectral utilization and adapt to heterogeneous traffic patterns under extreme mobility, necessitating a joint beam-frequency dynamic coordination framework. To address the challenges of multi-beam LEO systems, this paper introduces a hybrid framework that synergizes adaptive beam activation patterns and spectrum reuse optimization, enhanced by a deep reinforcement learning (DRL)-driven coordination mechanism for resource allocation in time. By dynamically adjusting beam activation patterns and frequency allocation, the framework optimizes spatial-temporal resource utilization while mitigating co-channel interference. Angular-constrained multi-criteria clustering achieves dynamic beam-user mapping with low-complexity adaptation for LEO mobility. The DRL-based component further coordinates multi-dimensional parameters, including transmit power and sub-channel assignment, to balance throughput and latency under time-varying channel conditions. Extensive simulations validate the framework’s capability to maintain high spectral efficiency and coverage performance across diverse scenarios, outperforming conventional static allocation methods. The results highlight its adaptability to dynamic traffic patterns and scalability for large-scale deployments, providing a robust foundation for next-generation LEO systems.

Hyperspectral imagery is valuable for accurate detection of land-cover changes within a consistent area across time. However, current training paradigms for hyperspectral change detection (CD) are usually in supervised form which are not consistent to heavy labeling cost reality. Moreover, current deep learning methods face limitations due to insufficient temporal dependencies’ sharing and inadequate densely bandwise spatial position dependencies. To tackle these challenges, we introduce an innovative semi-supervised training paradigm called Conflict HyperMatch and a deep learning model called temporal weight-sharing and bandwise spatial interacting transformer (TWBSIT) for hyperspectral CD. The key contributions of this study are as follows: 1) introduction of the Conflict HyperMatch training schedule, which relies on representation discrepancy and weak-to-strong prediction consistency, to improve the sample leveraging of both labeled and unlabeled data; 2) weight-sharing interacting temporal attention (WITA) module is contributed to capture shared temporal interactions and resemblances; and 3) bandwise cross-spatial attention (BCSA) module is introduced to achieve a more extensive spatial perception of the specific central image patch. Extensive experiments conducted on three real datasets validate the effectiveness of the proposed TWBSIT model based on Conflict HyperMatch in leveraging both labeled and unlabeled samples for hyperspectral CD. This method notably decreases the requirement for labeled training samples and surpasses the performance of many existing hyperspectral CD methods.

Deep learning technologies have been successfully applied to hyperspectral (HS) image classification with remarkable performance. However, compared with traditional machine learning methods, neural networks usually need more data. In remote sensing (RS) research, obtaining a large number of labeled HS data is very difficult and expensive work. Simultaneously, the distribution of feature information is bound to be unbalanced, and tends to conform to the long tail. At present, the neighborhood information of unlabeled samples is usually ignored in HS image classification tasks based on semi-supervised learning. In this letter, we propose a new semi-supervised long-tail learning framework based on spatial neighborhood information (SLN-SNI), which can complete the HS image classification task under unbalanced small sample data. Specifically, a new semi-supervised learning strategy is proposed. On this basis, a new method to determine the label of unlabeled samples based on spatial neighborhood information (SNI) is proposed. The coarse classification results divided into three situations are judged again, and the accuracy of pseudo labels is improved. The performance of the proposed method is tested on three public HS image datasets. Compared with the current advanced methods have achieved a certain improvement.

Due to the limitation of imaging equipment, it is difficult to acquire hyperspectral images with high spatial resolution directly. Existing approaches improve the resolution of HSIs by fusing multispectral image (MSI) and hyperspectral image (HSI). However, most of them are only feed-forward. They only learn low- to high-resolution feature mappings without considering the ill-posedness of super-resolution tasks, leading to a large solution space of mapping functions and making it difficult to learn a complete mapping function. Moreover, there is a large resolution difference between HSI and MSI, and some up-sampling operations are inevitably employed in the network. Nevertheless, traditional upsampling methods only represent pixel points in a discrete way, failing to adequately restore the continuous spatial and spectral information. To this end, this paper proposes a spatial-spectral implicit neural representation network for hyperspectral and multispectral image fusion (SS-INR). Inspired by the success of implicit neural representation(INR) in continuum reconstruction, we design spatial-INR and spectral-INR for spatial and spectral resolution reconstruction, respectively. SS-INR contains two processes: forward fusion (FF) and back-projection fusion(BPF). In the FF process, the input HSI is first spatially upsampled with Spatial-INR to overcome spatial resolution differences while performing initial fusion with MSI. In the BPF process, we explore the spatial and spectral degradation processes and use them as prior knowledge for error correction. Extensive experiments on five public hyperspectral datasets demonstrate the effectiveness of SS-INR, and SS-INR achieves competitive results compared with existing state-of-the-art fusion methods. The source code for SS-INR will be released at https://github.com/wxy11-27/SS-INR.

Improving the spatial resolution of hyperspectral (HS) images is of great significance for the subsequent applications.1As the multispectral (MS) image can provide abundant complementary land-cover spatial information, hyperspectral and multispectral image fusion (HMF) have become a mainstream to generate HS images with both high spatial and spectral resolution. HMF has witnessed rapid progress by leveraging dictionary learning technique. However, existing approaches are highly sensitive to the image registration accuracy, and the reconstruction performance of the non-overlapped spectral bands between HS and MS image are extremely limited. To alleviate the effect of image misregistration and enrich the spectral information of non-overlapped bands, a general HMF dictionary learning framework which considers non-overlapped spectral bands reconstruction and image misregistration is proposed in this paper. For registration error, the proposed method is rectified by the improved dictionary learning, which can solve the problem of the spectral information matching gap existing in traditional HMF methods between HS image with MS image. Meanwhile, for non-overlapped spectral bands reconstruction, a novel coefficient optimization strategy is adopted to improve the non-overlapped bands reconstruction. Therefore, the registration error can be avoided to greatest extent and the accuracy of non-overlapped bands reconstruction can be effectively improved. Experiments both on simulated and real-world datasets demonstrate that the proposed method can effectively tackle the registration error problem and increase HMF accuracy with different spectral range. Meanwhile, the proposed framework provides guidance significance for the dictionary learning based HMF methods with various constrains to improve the non-overlapped bands reconstruction accuracy.

Deep learning methods have been extensively applied to hyperspectral (HS) image change detection task and achieved promising performance. However, the beneficial joint spatial-spectral-temporal information provided by the HS images has not been fully used. Since the bi-temporal HS images are highly symmetric, in this letter, we propose a novel Cross-Temporal Interaction Symmetric Attention Network (CSANet), which can effectively extract and integrate the joint spatial-spectral-temporal features of the HS images, at the same time to enhance feature discrimination ability of the changes. Specifically, we propose a novel Cross-Temporal Interaction Symmetric Attention (CSA) module to interact the bi-temporal HS information, where self attentions are combined to enhance the feature representation ability of each temporal image, and the cross-temporal attention is utilized to integrate the difference features oriented from each temporal feature embeding. On this basis, we design a siamese network structure equipped with the CSA to hierarchically extract the change information in a symmetric pattern. Experimental results on three public HS image change detection datasets show that the proposed CSANet change detection framework achieves a significant improvement when comparing with the state-of-the-art methods. The source code of the proposed framework will be released at https://github.com/srxlnnu.

Convolutional Neural Networks (CNNs) have been extensively applied to hyperspectral (HS) image classification tasks and achieved promising performance. However, for CNN based HS image classification methods, it is hard to depict the dependencies among HS image pixels in long-range distanced positions and bands. Moreover, the limited receptive field of the convolutional layers extremely hinders the development of the CNN structure. To tackle these problems, in this paper, the novel Bottleneck Spatial-Spectral Transformer (BS2T) is proposed to depict the long-range global dependencies of HS image pixels, which can be regarded as a feature extraction module for HS image classification networks. More specifically, inspired by Bottleneck Transformer in computer vision, for HS image feature extraction, the proposed BS2T is incorporated with a feature contraction module, a multi-head spatial-spectral self-attention (MHS2A) module and a feature expansion module. In this way, convolutional operations are replaced by the MHS2A to capture the long-range dependency of HS pixels regardless of their spatial position and distance. Meanwhile, in the MHS2A module, to highlight the spectral features of HS images, we introduce the spectral information and content spatial positional information to classical multi-head self-attentions to make the attentions more positional aware and spectral aware. On this basis, a dual-branch HS image classification framework based on 3D CNN and BS2T is defined for jointly extracting the local-global features of HS images. Experimental results on three public HS image classification datasets show that the proposed classification framework achieves a significant improvement when comparing with the state-of-the-art methods. The source code of the proposed framework can be downloaded from https://github.com/srxlnnu/BS2T.

Change detection techniques of hyperspectral images(HSI) has witnessed great improvements with the applications of deep convolutional networks (CNN). In this paper, we propose a novel HSI change detection framework based on 3D-Wavelet domain active convolutional neural network. First, the bi-temporal hyperspectral difference image is decomposed into directional subbands by the discrete 3D-Wavelet transform, which is capable of suppressing the noise information of the HSIs. Then, to enhance the change discrimination ability of the primary features, the directional subbands are concatenated from coarse to fine scales to form the initial 3D-Wavelet feature map. In the conventional implementation, active learning strategy is iteratively adopted to extract the deep features of the constructed feature map, in each active learning round, the most informative unlabeled samples will be selected to enlarge the training set, which greatly reduces the labor of annoteing data. Results on two realworld hyperspectral change detection datasets demonstrates the effectiveness of the proposed approach.

The high spectral resolution of hyperspectral (HS) images provides the possibility of omnidirectional feature identification of objects. However, the high-dimensional features and the high redundancy information properties make data processing and the application of HS images extremely challenging. Thus, effectively expressing and correlating the intrinsic correlations of HS images by establishing a statistical model is of great significance. This article proposes a nonsubsampled shearlet transform hidden Markov forest (NSST-HMF) model. This new approach has three key characteristics: 1) the statistical properties of the NSST coefficients are studied in the spatial and spectral directions, respectively, and the “clustering” and “aggregation” properties are observed in both directions; 2) the HMF structure is proposed to depict the multidimensional collaborative correlation of the HS image NSST coefficients, and the proposed method considers the multidirectional transfer relationships among the Markov structure of HS images NSST coefficient for the first time, which significantly improves the prediction ability of the model; and 3) a novel HS-pansharpening approach based on the NSST-HMF model and amplitude modulation of large state probability in the high-frequency subband direction region is proposed. Experimental results show that our method can efficiently improve the spatial resolution of HS images while simultaneously preserving their spectral features. The HMF structure is first proposed in this article, which provides a way to depict the collaborative correlation of multichannel images.