The continuous development of Earth observation (EO) technology has significantly increased the availability of multi-sensor remote sensing (RS) data. The fusion of hyperspectral image (HSI) and light detection and ranging (LiDAR) data has become a research hotspot. Current mainstream convolutional neural networks (CNNs) excel at extracting local features from images but have limitations in modeling global information, which may affect the performance of classification tasks. In contrast, modern graph convolutional networks (GCNs) excel at capturing global information, particularly demonstrating significant advantages when processing RS images with irregular topological structures. By integrating these two frameworks, features can be fused from multiple perspectives, enabling a more comprehensive capture of multimodal data attributes and improving classification performance. The paper proposes a spatial-spectral-structural feature fusion network (S3F2Net) for HSI and LiDAR data classification. S3F2Net utilizes multiple architectures to extract rich features of multimodal data from different perspectives. On one hand, local spatial and spectral features of multimodal data are extracted using CNN, enhancing interactions among heterogeneous data through shared-weight convolution to achieve detailed representations of land cover. On the other hand, the global topological structure is learned using GCN, which models the spatial relationships between land cover types through graph structure constructed from LiDAR data, thereby enhancing the model’s understanding of scene content. Furthermore, the dynamic node updating strategy within the GCN enhances the model’s ability to identify representative nodes for specific land cover types while facilitating information aggregation among remote nodes, thereby strengthening adaptability to complex topological structures. By employing a multi-level information fusion strategy to integrate data representations from both global and local perspectives, the accuracy and reliability of the results are ensured. Compared with state-of-the-art (SOTA) methods, the framework’s validity is verified on three real multimodal RS datasets. The source code will be available at https://github.com/slylnnu/S3F2Net.

The acquisition of multisource remote-sensing (RS) data has become more and more convenient due to the boom and innovation of RS imaging technology. The fusion and classification of hyperspectral images (HSIs) and Light Detection and Ranging (LiDAR) data has become a research hotspot because of their excellent complementarity and the vigorous development of deep learning (DL) provides effective methods. Most of the existing methods based on convolution neural networks (CNNs) have fixed convolution kernels, making it difficult to extract multiscale detailed features. In this letter, we propose a multiscale pyramid fusion framework based on spatial–spectral cross-modal attention (S2CA) for HSIs and LiDAR classification, which has strong multiscale information learning ability, especially in areas with complex information changes, thereby improving classification accuracy. Multiscale pyramid convolution is used to extract multiscale features, and an effective feature recalibration (EFR) module is used to enhance features and suppress useless information at each scale. To increase the interactivity of information between modes, we propose an S2CA module, which uses the features of different modes to enhance each other. Three real public datasets are used for the experiment. Compared with the existing advanced methods, the proposed method achieves the best results. The source code of the multiscale S2CA network (MS2CANet) will be public athttps://github.com/junhengzhu/MS2CANet.

Multisource remote sensing (RS) image fusion leverages data from various sensors to enhance the accuracy and comprehensiveness of Earth observation. Notably, the fusion of hyperspectral (HS) images and light detection and ranging (LiDAR) data has garnered significant attention due to their complementary features. However, current methods predominantly rely on simplistic techniques such as weight sharing, feature superposition, or feature products, which often fall short of achieving true feature fusion. These methods primarily focus on feature accumulation rather than integrative fusion. The transformer framework, with its self-attention mechanisms, offers potential for effective multimodal data fusion. However, simple linear transformations used in feature extraction may not adequately capture all relevant information. To address these challenges, we propose a novel multimodal contrastive fusion transformer (MCFT). Our approach employs convolutional neural networks (CNNs) for feature extraction from different modalities and leverages transformer networks for advanced fusion. We have modified the basic transformer architecture and propose a double position embedding mode to make it more suitable for RS image processing tasks. We introduce two novel modules: feature alignment module and feature matching module, designed to exploit both paired and unpaired samples. These modules facilitate more effective cross-modal learning by emphasizing the commonalities within the same features and the differences between features from distinct modalities. Experimental evaluations on several publicly available HS-LiDAR datasets demonstrate that proposed method consistently outperforms existing advanced methods. The source code for our approach is available at:https://github.com/SYFYN0317/MCFT.

The hyper-sharpening technique fuses a high-resolution auxiliary image with a low spatial resolution hyperspectral (HS) image, aiming to enhance the spatial details of the HS image while preserving its spectral integrity. Although it extends from multispectral (MS) sharpening, the unique properties of HS data and the diversity of auxiliary images bring challenges to the application of MS sharpening methods. In this paper, a subspace dynamic combined sparse hyper-sharpening method for diverse auxiliary images is proposed. First, based on the dynamic total variation of MS sharpening, effective data reduction, and spectral coordination are realized by seeking reasonable subspace transformation paths and adopting band-matching processing for auxiliary images. Secondly, by associating the derivation of the relevant a priori with the residual representation, an idea of generalized “subspace + dynamic" regularization term is proposed. On this basis, a combination of regularization terms corresponding to dynamic gradient domain sparsity and dynamic nonlocal transform domain sparsity in subspace is explored. Finally, the alternating direction multiplier method and the improved closed-form solution are used for optimization. The general effectiveness of the proposed method is verified in three types of experiments (HS-PAN, HS-MS, HS-HS) on four public datasets. In the HS-HS class of experiments, where the inter-source correlation is low, the proposed method can overcome the spectral degradation assumption failure problem, with a PSNR improvement of 5.14% to 131.79% and an ERGAS improvement of 21.87% to 99.39% compared to the other nine methods. The code is at https://github.com/JZ-Tao/SDCS.

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.

Hyperspectral (HS) image change detection (CD) is an integral component of multitemporal remote-sensing (RS) Earth observation research. However, the existing HS image CD technology still has some problems, such as insufficient effective information extraction and weak correlation between shallow information and deep information, and so on. This letter proposes a new approach called attention-guided feature fusion network for multitemporal HS image CD (AgF2Net). This method is capable of efficiently retrieving and combining spatial–spectral (SS) features extracted from both shallow and deep layers of HS images, thereby enhancing the network’s capability to capture features from multitemporal HS images. The attention-guided enhanced joint feature extraction strategy is used to obtain a better change discriminative feature representation, and the multilevel features extracted from the backbone network are combined between spatial information and spectral information. The integrated channel feature fusion module (ICFFM) not only solves the problem of insufficient feature fusion at different levels, but also strengthens effective semantic information and forms features with more discriminative ability, while also realizing the advantages of multiple features and enhancing the network’s robustness and the accuracy of CD results. According to experimental results obtained from three publicly available datasets for detecting changes in HS images, the findings indicate that the proposed AgF2Net outperforms most advanced state-of-the-art (SOTA) methods. The source code of the AgF2Net will be public onhttps://github.com/NWH/AgF2Net.

With the continuous improvement of satellite sensor performance, it is becoming easier to obtain different types of remote sensing (RS) data from multiple sensors, and the fusion of hyperspectral (HS) images and light detection and ranging (LiDAR) for land use/land cover classification has become a research hotspot. However, the current mainstream methods still have defects in feature extraction and feature fusion. In the feature extraction stage, previous methods usually use a single-scale patch as input and a fixed convolution kernel for feature extraction, which makes it difficult to extract features in line with different land cover types at the same time and to obtain high-quality features. Although multi-scale feature extraction can solve the one-sidedness problem of single-scale features, it also brings the challenge of high-dimensional multi-scale features. In the feature fusion stage, the current fusion methods are relatively simple. Therefore, we propose a dynamic scale hierarchical fusion network (DSHFNet) for fusion classification of HS images and LiDAR data. By calculating the similarity in the scale space and judging the information at different scales through the threshold value, the appropriate scale features are dynamically selected, the small-scale features are integrated into the large-scale features, and the dimensionality of the features is reduced. This method solves the unreliability problem of single-scale features and the high dimension problem of multi-scale features. In the feature fusion process, different attention modules are used for hierarchical fusion, spatial attention modules are used for shallow fusion and joint feature extraction, and modal attention modules are used for deep fusion of joint features and features from different sensors to achieve complete complementarity of features. Experimental evaluations on three real RS datasets demonstrate the superiority of the proposed method compared to existing methods. The source code can be downloaded at https://github.com/SYFYN0317/DSHFNet.

The demand for satellite relay service is increasing, while the satellite network resources are limited and unevenly distributed, which pose a great challenge to task scheduling of tracking and data relay satellites. Most existing relay scheduling models are based on static antenna setup time, which has limitations in practical applications and leads to ineffective utilization of satellite resources. This paper models the task scheduling problem based on dynamic antenna setup time and splittable tasks, which maximizes the total scheduled task number and minimizes the total antenna setup time. We also propose a two-stage insertion heuristic to solve the problem. The experimental results show that the proposed algorithm can significantly improve the total scheduled task number, total antenna setup time and effective time window utilization compared with traditional methods.

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.

Energy consumption is becoming a key issue in the research of future network. In practice, network traffic has a periodic time distribution that occurs most often at a low level. This feature provides the possibility of achieving network energy savings through topology switching. By considering the deficiencies in existing studies, such as the low adaptability between network working topology and traffic load, the abnormal topology switching caused by abnormal and unbalanced traffic, and the low reliability of energy-saving topology, this paper proposes an energy-efficient routing method for software-defined networks based on topology switching and reliability. The method involves two parts: a topology-switching method and a failure recovery method. The former adapts the network working topology to the network traffic demands through dynamic topology switching to decrease the network energy consumption. The latter adopts an active strategy for fast fault recovery to ensure network reliability in the energy-efficient topology. Two network typologies and their traffic data are used to experimentally verify the method. The results show that, compared with the static topology switching method TLS, the energy saving of the proposed method can be improved at most 2.07 times and 4.63 times in two typical typologies, respectively, while ensuring network reliability.

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.

Fused lasso norm is classically adopted to model sparse piecewise constant signals, however it is not the convex hull of the best representation of such simultaneously structured signal. In this paper, we propose a convex variational norm for better modeling sparse piecewise constant signals. The norm is based on (1) promoting sparsity in first-order difference with total variation norm and (2) exploiting latent group structure in first-order difference with simple linear constraints. We demonstrate the proposed norm outperforms fused lasso norm in a denoising setup with numerical experiments.