In recent years, with the continuous advancement of Earth observation technologies, the joint utilization of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) for classifying complex land cover types has garnered widespread attention in the field of multi-source remote sensing. However, the diversity of land cover increases the complexity of the spatial and spectral structures in remote sensing data, creating challenges for extracting discriminative features effectively. Moreover, the heterogeneity of multi-source remote sensing data poses challenges for existing methods to effectively integrate complementary information from various data sources. To address these challenges, a multi-scale split-recombination cooperative fusion network (MSRCFNet) is proposed for joint land cover classification using HSI and LiDAR data. It consists of three main components: multiple parallel multi-scale hierarchical inverted-pyramid (MHIP) modules, a cross-modal cooperative fusion module (CCFM), and a classification module. The MHIP module comprises multiple convolutional split-recombination blocks (CSRBs) at various scales, designed to extract and fuse discriminative multi-scale features. CCFM leverages spatial-scale consistency and the self-attention mechanism to model global relationships between different modalities, and then applies the cross-attention mechanism to effectively integrate complementary information from heterogeneous data. The classification module transforms the fused features into the final classification results. Experimental results on three publicly available HSI-LiDAR datasets demonstrate the superiority of the proposed network over state-of-the-art methods.

The joint classification of hyperspectral images (HSI) and LiDAR data is an important research direction in the field of remote sensing image processing. However, existing methods are mostly limited to inadequate feature extraction or simple serial fusion strategies, failing to fully exploit the deep cross-modal relationships between LiDAR and HSI data. Meanwhile, global modeling based on Transformers, despite capturing long-range dependencies, faces difficulties in handling high-dimensional remote sensing data due to quadratic computational complexity, leading to constrained global-local feature collaboration. To address these issues, this paper proposes a novel hierarchical multi-scale Mamba network (HMMamba) for joint classification of HSI and LiDAR data. First, the method adopts a hierarchical design to obtain rich multi-level feature representations through multi-scale feature extraction. Second, it utilizes adaptive weight allocation to achieve dynamic cross-modal feature integration. Finally, it constructs a Fused Feature enhancement Mamba Block (FFMB), which integrates a dual-branch attention mechanism and selective state space modeling to achieve efficient global context modeling under linear computational complexity Extensive experiments on four public datasets (Houston2013, MUUFL, Trento, and Augsburg) demonstrate that HMMamba significantly outperforms existing state-of-the-art methods in terms of classification performance. Specifically, on Augsburg dataset, the OA improvement is 3.24% compared to the suboptimal method, fully validating the superior performance of the proposed method. The codelink of the proposed method is https://github.com/leiyeqi/HMMamba.

When hyperspectral image classification encounters high-dimensional spectral channels, the feature utilization rate is often low due to significant redundancy between channels and uneven discriminatory power. Furthermore, different land cover types exhibit notable differences in scale and morphological changes across space. This structural diversity poses significant challenges to spatial feature modeling. To address this problem, this paper proposes a novel framework based on the Mamba model-PS2Mamba for hyperspectral image classification. This framework integrates three strategies: spectral fine modeling, global perceptual scale adaptation, and multi-scale spatial structure modeling. Firstly, this paper designs a statistically enhanced normalized band refinement (SENBR) module, which dynamically enhances or suppresses channel features based on channel correlation, variability, and importance, effectively suppressing redundant and noisy bands. Secondly, a global-aware scale adaptation (GASA) module is proposed. By incorporating a scale scoring network and a multi-directional modeling mechanism, this module enables adaptive perception and directional enhancement modeling of spatial structures at different scales. Finally, a lightweight multi-scale spatial structure extraction module, LightPyramid, is constructed. By enriching spatial semantic information through multi-branch parallel convolutions, it enhances the model’s ability to represent complex surface structures, while maintaining spatial resolution. Experimental results on four representative hyperspectral datasets, including Pavia University, Salinas, and two UAV-based datasets (HongHu and HanChuan), demonstrate that PS2Mamba achieves overall accuracies of 98.77%, 99.61%, 96.20%, and 96.01%, respectively. Compared with existing CNN, GCN, Transformer, and Mamba based models, PS2Mamba achieves up to 12.91% improvement in accuracy, showing superior generalization and robustness, particularly under small-sample conditions.

Multi-source remote sensing image classification based on Mamba has received increasing attention. However, existing methods do not consider the non-causal characteristics of visual data, which leads to insufficient extraction of global features by the model. To alleviate this problem, a novel network called TBi-Mamba is proposed for joint classification of hyperspectral and LiDAR data. Firstly, a cross-modal knowledge search module (CMKS) is designed, which effectively captures local features in different modalities through multi-scale feature extraction and interaction between multi-modal data. Secondly, a triple bidirectional sequence scanning mamba module (TBi-M) is proposed, which comprehensively considers multimodal information from the perspective of bidirectional sequence scanning, and introduces Mamba to accurately model global dependencies. Finally, a mixed feature reconstruction module (MFRM) is constructed. This module constructs an auxiliary loss function by reconstructing images of different modalities, providing more comprehensive supervision information and thus improving the performance of the model. The proposed method was evaluated on three publicly available datasets, and experimental results fully demonstrated that the classification performance of the proposed method is superior to that of some state-of-the-art (SOTA) methods.

Band selection plays a crucial role as a preprocessing step in hyperspectral image processing tasks. Currently, most band selection methods tend to fuse spatial spectral features, directly from the original spectral domain, without fully exploring the distinct information embedded in different frequency components of the spectrum. In this study, a greedy band selection strategy based on dual-frequency collaborative feature fusion (GSDFC) is proposed for unsupervised band selection of hyperspectral images. Firstly, a dual-frequency collaborative feature fusion module (DFCFFM) is designed to extract and fuse the spatial spectral features of two frequency domain components separately. Secondly, to further enhance the effectiveness of DFCFFM and learn important features in low-frequency components adaptively, a grouping spatial attention module (GSAM) is introduced, which effectively captures global and local spatial dependencies. Finally, a greedy selection strategy is constructed by utilizing the mutual information (MI) of the fused features and the Pearson correlation coefficient (PCC) of the original data to select the optimal subset of bands. The proposed GSDFC band selection method is validated by classification. Extensive experiments have shown that, compared with some advanced methods, the proposed GSDFC method can achieve the best classification performance on three public datasets: Indian Pines, Pavia University, and Houston 2013, which fully demonstrates the effectiveness of the proposed GSDFC method. The related code will be released at https://github.com/Zengzx716/GSDFC.

The rapid development of sensor and multimodal technology has provided more possibilities for multisource remote sensing image classification. However, some existing joint classification methods are limited to single-level feature fusion and fail to fully explore the deep correlation between cross-level features, thus limiting the effective interaction and complementarity of information between different modal data. To alleviate this issue, this article proposes a hierarchical multimodal feature aggregation-based multihead axial attention transformer (HMAT) for joint classification of hyperspectral and light detection and ranging (LiDAR) data. First, a hierarchical multimodal feature aggregation module (HMFA) is proposed to more effectively fuse spatial–spectral features of hyperspectral images (HSIs) and elevation features of LiDAR data and generate more discriminative low-dimensional feature representations. Second, a pyramid-inverted pyramid convolution module (PIP) is designed. Through the complementary feature extraction structure, PIP can more fully capture the multiscale local features in the fused feature map of hyperspectral and LiDAR data. Finally, a multihead axial attention (MHAA) component is constructed to capture information at different scales in the fused feature maps, thereby accurately modeling global dependencies. The proposed HMAT has been extensively tested on three publicly available datasets. The experimental results demonstrate that the classification performance of the proposed method outperforms that of several state-of-the-art methods.

Remote sensing images obtained at high altitudes often contain complete object or scene information, which makes their global visual features richer compared to natural images. In order to enhance the scope and multilevel characteristics of global visual features of remote sensing images, this article proposes an enhanced global feature-guided network based on multiple filtering noise reduction (GFRNet) for remote sensing image compression. First, a pyramid vision transformer (PVT) is introduced into remote sensing image compression for the first time. Based on this, a PVT compression branch (PVTCB) is designed, which can capture multilevel global visual features through a three-stage pyramid transformer module for image compression (TPTC) and utilizes filters to accurately control the output of TPTC. Second, a quadruple-filtered multicore noise reduction attention module (QFMR-AM) is constructed in the four-stage compression branch (FSCB) for denoising and enhancing multilevel features. Finally, a global visual feature guidance module (GVGM) is designed between FSCB and the four-stage reconstruction decoder (FSRD). By calculating the global visual feature loss LossGVF through GVGM, a novel rate-distortion LossTotal is constructed, making the network more focused on extracting global information. Experimental results show that compared with some advanced methods, the proposed GFRNet achieves better compression performance on multiple evaluation indicators. In addition, the reconstructed images obtained by the proposed GFRNet can provide better classification performance, which further proves that the proposed method helps to preserve more important features of remote sensing images during the compression process.

Remote sensing image compression networks aim to enhance the similarity between the input image and the reconstructed image. The current network rarely considers the potential relationship between the compression features of different levels and the reconstruction features of the corresponding levels, which limits the improvement of remote sensing image compression performance. In this article, a concept of multilevel domain similarity is first proposed, which fully develops the multilevel domain similarity between the encoding and decoding processes to improve the quality of reconstructed images. On this basis, a multilevel domain similarity enhancement guided network (MDSNet) is proposed for remote sensing image compression. First, an efficient compression baseline network (BaselineA) was proposed, which realizes efficient image compression with low computational complexity. Second, a multilevel domain similarity enhancement module (MDEM) was designed, which improved the quality of the reconstructed image by enhancing the multilevel domain similarity. Third, a global information-enhanced attention module (GIE-AM) was constructed to enhance channel features and global features. Finally, under the guidance of the total loss (LossTotal), which is constructed by the proposed MDEM loss (MDEM-Loss), an effective compression was implemented by the whole network for remote sensing image compression. Experimental results show that compared with some advanced compression models, the proposed MDSNet can significantly improve compression performance with lower computational complexity. In addition, the reconstructed images obtained by the proposed method can provide better classification performance, which further proves that the proposed MDSNet helps to preserve more important features of remote sensing images during the compression process.

In recent years, convolutional neural networks (CNNs) have achieved great success in hyperspectral image (HSI) classification tasks. CNNs focus more on the local features of HSIs. The recently emerging Transformer network has shown great interest in the global features of HSIs. However, existing Transformer networks only consider single-scale feature extraction and do not combine the advantages of multiscale feature extraction and Transformer global feature extraction. To address this issue, this article proposes a dual-branch multiscale Transformer (DBMST) for HSI classification. First, a large-size spectral convolution kernel is utilized for the spectral dimension of the hyperspectral cube for downsampling feature extraction. Next, a channel shrink soft split module (CS3M) is proposed, which not only solves the problem of missing local information in large-scale tokens but also extracts shallow features and performs dimensionality reduction on channels. Then, considering the different dimensions of features extracted at different scales in two branches, a pooled activation fusion module (PAFM) is carefully designed. Finally, the proposed DBMST is evaluated on three commonly used HSI datasets. The experimental results show that DBMST achieves better classification performance compared to other advanced networks, demonstrating the effectiveness of the proposed method in HSI classification.

In recent years, transformer has attracted the attention of many researchers in the field of remote sensing due to its ability to model global information. However, it is difficult to extract local features such as textures and edges of images, thereby limiting the performance of transformer-based hyperspectral image classification (HSIC). Currently, most existing transformer models for HSIC improve their performance by combining the powerful feature extraction ability of convolution, which also introduces a large number of trainable parameters and increases model complexity. To address this issue, this article proposes a dual attention transformer for attention head interaction (DAHIT) for HSIC. First, a spatial local bias module (SLBM) was designed in the spatial branch, which introduces local priors to extract local features effectively without introducing numerous trainable parameters. Then, an attention head interaction module (AHIM) was proposed, which can make the interaction of information obtained by different attention heads. Finally, a diagonal mask multiscale dual attention module (DAM) was constructed in the spectral branch to enhance the attention to the correlation among different spectral bands through diagonal masks and then to extract features at different scales through feature vectors. Through a series of experiments, the proposed DAHIT is evaluated on four commonly used HSI datasets. The experimental results show that compared with other advanced methods, the proposed DAHIT method exhibits excellent classification performance, demonstrating the effectiveness of the proposed method in HSIC.

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification due to their ability to extract image features effectively. However, under the condition of limited samples, the modeling ability of CNNs for the relationships among samples is limited. At present, research on the classification of HSIs with a small number of samples remains an important challenge in the field of HSI processing. Recently, graph convolutional networks (GCNs) have been applied in HSI classification tasks. In this article, a multihop graph rectifies attention and spectral overlap grouping convolutional fusion network (MRSGFN) for HSI classification is proposed. In the graph convolution branch, a multihop graph rectify attention (MHRA) is designed to weight and correct the features extracted by graph convolution. In the convolutional branch, to solve the problem of dimensionality disaster caused by high spectral dimension with a small number of samples, a spectral intra group inter group feature extraction module (SI2FEM) based on spectral overlap grouping is constructed. In order to better fuse the features extracted from CNNs and GCNs, a Gaussian weighted fusion module (GWFM) is elaborately designed in this article. The features extracted by different branches are assigned different weights by GWFM through a 2-D Gaussian map and then fused. Numerous experiments were conducted on three common datasets and showed that the classification performance of the proposed MRSGFN is superior to other advanced methods.

For hyperspectral image classification (HSIC), labeling samples is challenging and expensive due to high dimensionality and massive data, which limits the accuracy and stability of classification. To alleviate this problem, a greedy strategy guided graph self-attention network (GS-GraphSAT) is proposed. First, a graph self-attention (GSA) mechanism is designed by combining a multihead self-attention (MHSA) mechanism with the graph attention network (GAT), which can simultaneously consider the direct and indirect relationships between nodes and deeply analyze the intrinsic characteristics of nodes. Second, a multiattention fusion (MAF) module is developed, which utilizes multiscale convolution kernels and attention mechanisms to significantly enhance the network’s ability to extract local features from images at the pixel level, thereby further enriching the hierarchy and diversity of features. Finally, a greedy training strategy (GTS) is proposed. During the training process, GTS accurately determines the optimal time to supplement samples by analyzing the changes in losses, thereby achieving a significant improvement in network classification performance with limited samples. Extensive experiments were conducted on four challenging datasets. The results demonstrate that the proposed method significantly outperforms other state-of-the-art methods in terms of classification accuracy and robustness. The performance improvement of overall accuracy (OA) can reach up to 1.70% in Houston 2013 (HT). The codes of this work will be available athttps://github.com/Isee-max/IEEE_TGRS_GS-GraphSATfor reproduction.

In the past, deep learning (DL) technologies have been widely used in hyperspectral image classification tasks. Among them, convolutional neural networks (CNNs) use fixed size receptive field (RF) to obtain spectral and spatial features of hyperspectral images (HSIs), showing great feature extraction capabilities, which are one of the most popular DL frameworks. However, the convolution using local extraction and global parameter sharing mechanism pays more attention to spatial content information, which changes the spectral sequence information in the learned features. In addition, CNN is difficult to describe the long-distance correlation between HSI pixels and bands. To solve these problems, a spectral-spatial fusion Transformer network (S2FTNet) is proposed for the classification of hyperspectral images. Specifically, S2FTNet adopts the Transformer framework to build a spatial Transformer module (SpaFormer) and a spectral Transformer module (SpeFormer) to capture image spatial and spectral long-distance dependencies. In addition, an adaptive spectral-spatial fusion mechanism (AS2FM) is proposed to effectively fuse the obtained advanced high-level semantic features. Finally, a large number of experiments were carried out on four datasets, Indian Pines, Pavia, Salinas and WHU-Hi-LongKou, which verified that the proposed S2FTNet can provide better classification performance than other the state-of-the-art networks.

The emergence of convolutional neural networks (CNNs) has greatly promoted the development of hyperspectral image classification (HSIC). However, some serious problems are the lack of label samples in hyperspectral images (HSIs), and the spectral characteristics of different objects in HSIs are sometimes similar among classes. These problems hinder the improvement of HSIC performance. To this end, in this article, a positive feedback spatial-spectral correlation network based on spectral interclass slicing (PFSSC_SICS) is proposed. First, a spectral interclass slicing (SICS) strategy is designed, which can remove similar spectral signature between classes and reduce the impact of similar spectral signature of different classes on HSIC performance. Second, in order to solve the impact of the lack of labeled samples on HSIC, a positive feedback (PF) mechanism and a spatial-spectral correlation (SSC) module are introduced to extract deeper and more features. Finally, the experimental results show that the classification performance of the PFSSC_SICS is far exceed than that of some state-of-the-art methods.

In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image classification (HSIC). However, the size of the convolutional kernel in CNNs is fixed, which makes it difficult to capture the dependence of long-range feature information. In addition, the extracted features often contain a large amount of redundant information. In order to alleviate these issues, a feature complementary attention network based on adaptive knowledge filtering (FCAN_AKF) is proposed in this paper. First, in order to alleviate the problem that CNNs are difficult to capture the dependence between close-range and long-range spectral features due to the limited receptive field, a non-local band regrouping (NBR) strategy is designed. NBR enables CNN to capture nonlocal spectral features in a limited receptive field to establish the interdependence between close-range and long-range spectral features. In addition, the non-local features extracted after using NBR and the local features of the original hyperspectral image are integrated to achieve complementation between non-local features and local features. Then, in order to eliminate the interference of redundant information on the network, a dual pyramid spectral spatial attention (DPSSA) module is proposed and used to capture spectral spatial attention. Next, an adaptive knowledge filter (AKF) is designed, which can adaptively further filter out redundant information and enhance feature information that is beneficial for classification. Finally, extensive experiments were conducted on three challenging datasets, demonstrating that the proposed method has stronger competitiveness compared to some state-of-the-art HSIC methods.

In recent years, convolutional neural networks (CNNs) have achieved excellent performance in hyperspectral image classification and have been widely used. However, the convolution kernel used in traditional CNN has the limitation of single scale which is not conducive to the improvement of hyperspectral classification performance. In addition, training a classification network of high-dimensional data based on limited labeled samples is still one of the challenges of hyperspectral image classification. To solve the above problems, a hyperspectral image classification method based on expansion convolution network (ECNet) is proposed. The expansion convolution injects holes into the standard convolution kernel to expand the receptive field (RF), so as to extract more context features. Because the shallow features of hyperspectral images contain more location and detail information, while the deep features contain stronger semantic information, in order to further enhance the correlation between deep and shallow information, inspired by ResNet, a similar feedback block (SFB) is introduced on the basis of ECNet, and the deep features and shallow features are fused through this feedback mechanism. Thus, an improved version of ECNet method is obtained, which is called FECNet. This study was tested on four commonly used hyperspectral data sets (i.e. Indian Pine (IP), Pavia University (UP), Kennedy Space Center (KSC), Salinas Valley (SV)) and on a higher resolution and complexly distributed land cover data set (University of Houston (HT)). The experimental results show that the proposed method has better classification performance than some state-of-the art methods, which shows that FECNet has a certain potential in hyperspectral image classification.

Aiming at the large error and low accuracy of wireless sensor node location, this paper proposes a node location method based on the fusion of Particle Swarm Optimization and Monkey Algorithm (PSO-MA). Firstly, this article describes the node location model based on DV-HOP algorithm; secondly, this article uses PSO in node location, uses place Laplace distribution for population initialization, improves population diversity, and optimizes particle weights to avoid algorithm falling into local optimality. In this paper, dynamic guidance factors are used to update individual positions to improve individual optimization capabilities, and Monkey Algorithm is used to select individuals to improve the quality of optimal solutions. In the simulation experiment, the algorithm PSO and MA of this paper are compared to achieve better positioning results in the reference node ratio, node density and communication radius indicators.

Most of the compression methods for remote sensing images are often designed under the guidance of mean square error. However, for the vision-related applications, high peak-signal-to-noise ratio (PSNR) does not mean good visual quality. On the other hand, existing compression methods that considering the human visual system (HVS) are usually designed for natural images, without taking the unique characteristics of remote sensing images into account. Focusing on this problem, we present a novel HVS-based adaptive scanning (HAS) scheme for the compression of remote sensing images. First, after the wavelet transform, a retina-based visual sensitivity model is established, and then, the visual weighting mask is generated. Second, for the weighted transformed image, an adaptive scanning method is proposed, which provides different scanning orders among subbands and within a subband, respectively. The former focuses on organizing the codestream according to the importance of weighted subbands, and the latter aims at preserving the direction information of an image as much as possible. Finally, the binary tree codec is utilized. Experimental results show that, as compared with other scan-based compression methods, the proposed HAS-based compression method can provide better visual quality, which makes it more desirable in vision-related applications for remote sensing images.

Classification is one of the most important applications in the field of remote sensing. How to improve the accuracy of classification is the critical topic that has long obsessed the researchers. In this paper, a fusion method based on a synergic use of hyperspectral data and Polarimetric SAR (PolSAR) data is presented. This method consists of two main parts, feature-level fusion and decision-level fusion. In feature-level, parallel feature combination strategy is introduced to classification of remote sensing images. Results of feature-level fusion are used as inputs of decision-level fusion based on fuzzy set theory. The final results are compared with processing of single level and single data set, and it shows that the synergic method proposed in this paper has a superior performance in joint classification of hyperspectral and polarimetric SAR data.

Most lossy compression methods for hyperspectral image (HSI) usually compress the data in this way that focus on preserving low frequency information. However, for some applications such as edge detection, the information which belongs to high frequency is more useful. Thus, a new pixel of interest (POI)-preserving-based HSI compression scheme is proposed. The concept of POI is proposed because some pixels are significant in preserving the main high frequency. Firstly, the POI extraction is performed by unmixing and the mixed pixels are viewed as POI, then the mask of the pixel of interest (MPI) is generated. Secondly, the compression scheme based on the POI preserving is conducted. The spatial and spectral redundancies are reduced, respectively, then a POI-lifting strategy is adopted for preserving the main high frequency information. Finally, bit allocation and encoding to the transformed HSI is performed by SPIHT_TCIRA algorithm, followed by the contextual adaptive arithmetic coder (CAAC). Experiments are implemented using the HSI acquired by the ROSIS Sensor. Results indicate that compared with the common compression method, the POI-preserving-based compression method can keep the key high-frequency information more effectively.