Underwater image enhancement (UIE) aims to address image degradation caused by water absorption and scattering effects. Despite significant progress in deep learning-based UIE methods, existing approaches still face key challenges due to the neglect of physical imaging principle. Moreover, while current Mamba models achieve global modeling via multi-directional scanning, their local sequential strategy lacks sufficient global context. To this end, we propose a novel Physical Model-Guided Global Mamba (PGMamba) that combines the efficient sequential modeling capability of Mamba with underwater imaging physical model. Specifically, we first design a Spatial-Aware Global Mamba (SAGMamba) that achieves efficient long-range dependency modeling through a spatial-aware ranking strategy with global context information. Second, we develop a Physical Model-Guided Feed-Forward Network (PMGFFN) that explicitly incorporates underwater optical imaging principles into the network architecture. Extensive experimental results and comprehensive ablation studies demonstrate the outstanding performance and importance of our proposed method.

Hyperspectral image (HSI) reconstruction refers to the process of recovering the high-dimensional HSI signal from the measurements captured by various imaging systems. In the case of the coded aperture snapshot spectral imaging (CASSI) system, this involves recovering the HSI signal from snapshot measurements obtained using a coded aperture and disperser. However, previous methods for HSI reconstruction have been limited by the challenges of reconstructing complex high-dimensional data and the inevitable noise present in the measurements. To better capture the complex distribution of high-dimensional data and mitigate the impact of noise on reconstruction performance, this paper introduces an end-to-end approach leveraging a diffusion model, termed the hierarchically conditional diffusion model for HSI reconstruction (HDiff-HIR). HDiff-HIR achieves high-quality reconstruction by initializing with pure Gaussian noise and using a network to iteratively refine it. Additionally, we design a condition generation module, called the mask-integrated condition generation module (MCGM), which integrates 2D measurements with the coded aperture of the imaging system as conditions and hierarchically embeds them into the denoising network. Furthermore, within the network, we introduce a novel self-attention mechanism, named local-global spectral-enhanced multi-head self-attention (LGS-MSA), to efficiently capture long-range spatial dependencies in HSIs at relatively modest computational costs while incorporating fine-grained spectral features as complementary information. In LGS-MSA, we incorporate time embeddings to make it time-dependent, enabling it to capture both long-range spatial and temporal dependencies simultaneously. Through comprehensive experiments on both simulated and real datasets, we demonstrate that HDiff-HIR not only outperforms other advanced methods but also exhibits strong generalization capability. The code of HDiff-HIR is accessible: https://github.com/chenx2000/HDiff-HIR.

Multi-temporal hyperspectral imagery (HSI) has become a powerful tool for change detection (CD) owing to its rich spectral signatures and detailed spatial information. Nevertheless, the application of paired HSIs is constrained by the scarcity of annotated training data. While unsupervised domain adaptation (UDA) offers a potential solution by transferring change detection knowledge from source to target domains, two critical limitations persist: 1) the labor-intensive process of acquiring and annotating source-domain paired samples, and 2) the suboptimal transfer performance caused by substantial cross-domain distribution discrepancies. To address these challenges, we present a Temporal Self-Construction Cross-Domain learning (TSCCD) framework for UDA-based HSI-CD. Our TSCCD framework introduces an innovative temporal self-construction mechanism that synthesizes bi-temporal source-domain data from existing HSI classification datasets while simultaneously performing initial data-level alignment. Furthermore, we develop a reweighted amplitude maximum mean discrepancy (MMD) metric to enhance feature-level domain adaptation. The proposed architecture incorporates an attention-based Kolmogorov-Arnold network (KAN) with high-frequency feature augmentation within an encoder-decoder structure to effectively capture change characteristics. Comprehensive experiments conducted on three benchmark HSI datasets demonstrate that TSCCD achieves superior performance compared to current state-of-the-art methods in HSI change detection tasks. Codes are available at https://github.com/Zhoutya/TSCCD.

The high spectral resolution of the hyperspectral image (HSI) has facilitated the wide applications of anomaly detection techniques. However, existing HSI anomaly detection (HAD) methods usually deal with HSIs as a 2-D matrix and violate their inherent 3-D structures. Also, the limited spatial resolution often leads to intricate background-anomaly mixed subpixels, which makes accurate background learning and anomaly detection a challenge. To address the issues, this letter proposes to fully explore the inherent 3-D structural properties of HSIs and develop a coarse-to-fine tensor two-level decomposition (CTTD) method for HAD. Specifically, the primary decomposition is performed via tensor robust principal component analysis (TRPCA), to simultaneously discover the global tensor background component and the coarse group sparse anomaly component of HSIs. Then, the secondary decomposition with joint$\ell _{2,1,1}$and weighted nuclear norms (WNNs) is devised with the obtained global tensor background component in primary decomposition, to pursue the refined 3-D spatial-spectral sparse anomaly component. Finally, with the coarse-to-fine background learning and anomaly detection mechanism, the informative anomaly cues from the coarse group sparse and refined 3-D spatial-spectral sparse anomaly components are enhanced by weighted fusion, to achieve the final accurate detection result. Extensive experiments on various real-world HSI datasets verify the superior performance of our proposed CTTD than some state-of-the-art HAD methods.

Infrared small target detection (IRSTD) faces significant challenges due to low signal-to-noise ratios, poor contrast in infrared images, and the tendency for small and dim targets to be obscured by cluttered backgrounds. These factors complicate the extraction of diverse and effective information for target detection, with existing encoder-decoder architectures often causing irreversible loss of small target features through continuous down-sampling, resulting in missed and false detections. To address these challenges, we propose the Diverse Feature Capture and Harmonization Network (DFCHNet), which learns and harmonizes diverse features through multiple encoding paths. DFCHNet includes an infrared image reconstruction branch running in parallel with the detection branch, preserving small target information and reducing feature loss via complementary contextual encoding. Additionally, we introduce FTConv to capture target edges while suppressing background noise. A Cross-layer Feature Autonomous Selection (CFAS) method adaptively harmonizes cross-layer features. We also propose a Coordinate Calibration (CC) loss function and a two-stage training strategy to refine predicted target positions. Experimental results on three IRSTD datasets demonstrate that DFCHNet outperforms current state-of-the-art methods.

Recently, semi-supervised change detection (SSCD) has attracted considerable attention due to its remarkable capability to enhance model performance with limited annotated data. However, most existing SSCD methods rely primarily on pixel-level consistency learning to leverage unlabeled data. Although this approach can provide basic prediction consistency constraints, it exhibits notable limitations in capturing spatial continuity and global semantic coherence in structural change regions, and is easily affected by noise. To address this issue, we propose a novel SSCD framework, termed the local-global tri-consistency network (LGTC). LGTC incorporates a tri-consistency learning strategy at the pixel, region, and image levels, which provides complementary supervisory signals from fine-grained to global semantic scales, significantly enhancing representation ability and robustness on unlabeled data. Furthermore, a local-global interaction block (LGIBlock) is introduced to integrate local feature details with global context. Within it, the GFTBlock employs frequency domain attention to achieve efficient modeling of global information while effectively suppressing background noise, further enhancing the recognition performance of the model. Experimental results demonstrate that LGTC achieves state-of-the-art performance, attaining F1 scores of 87.12%, 88.65% and 83.25% on the LEVIR-CD, WHU-CD and CDD-CD dataset with 5% labeled data, respectively, fully verifying the effectiveness of the proposed method. Our source codes are available at https://github.com/sherryxu21/LGTC.

Hyperspectral images (HSIs) are rich in spectral information and are widely used in the field of land-cover classification. However, existing deep learning methods ignore the combination of multiscale and multiresolution information, while not making better use of light detection and ranging (LiDAR) elevation information to assist in the enhancement of HSI data. To solve the problem above, we proposed a multiresolution, multiscale local-global directional MLP (MMD-MLP) for HSI classification with a LiDAR-guided feature enhancement module in this article. The algorithm first introduced a local-global-directed MLP structure, which effectively combines local and global features. Second, a multiresolution and multiscale feature extraction strategy is invented for the accurate acquisition of the detailed information and features of different land covers under different scale sizes. Subsequently, a LiDAR-guided feature enhancement module, which introduces the elevation information from LiDAR for improving the feature representation of HSI, adopts a cross-attention mechanism to reduce the semantic gap to improve the features of HSI. The proposed algorithm was evaluated on multiple hyperspectral-LiDAR datasets, and the results demonstrate that it achieves state-of-the-art (SOTA) performance. The code will be available athttps://github.com/sanxian-svg/MMD-MLP.

Cross-view consensus representation plays a crucial role in hyperspectral image (HSI) clustering. Recently, multi-view contrastive cluster (MVCC) methods have leveraged contrastive loss to extract contextual consensus representations. However, these methods suffer from a critical limitation: MVCC frameworks often regard similar heterogeneous views as positive sample pairs while treating dissimilar homogeneous views as negative sample pairs. This misalignment leads to intra-class inconsistency and inter-class confusion. To address this problem, we propose a novel multi-view clustering method, termed Semantic Constraint-based Spatial-Spectral Multi-view Clustering (SCMVC). First, spatial views are designed to capture diverse features for contrastive clustering. Meanwhile, globally relevant information from the spectral view is extracted using a Transformer, which serves to enhance the representation of similar samples in the spatial multi-view. Then, SCMVC employs a semantic constraint-based joint loss function, comprising a semantic contrast loss and a semantic similarity consistency loss. The semantic contrast loss captures high-level, domain-invariant features from hyperspectral images, while the semantic similarity consistency loss enforces stricter constraints on the similarity of semantically related samples in feature space. Finally, SCMVC utilizes anchor points to guide similarity clustering, reducing randomness by predefining these points. This approach captures the directional characteristics of data, leading to a more stable clustering process. Abundant experiment studies on numerous benchmarks verify the superiority of SCMVC in comparison to some state-of-the-art clustering methods. The codes are available at SCMVC.

Hyperspectral target detection (HTD) is an approach to identify targets of interest in a scene by utilizing prior target spectra. Existing deep learning-based HTD methods usually need to generate a large number of samples for network training, and these generated samples often suffer from distortion. In addition, most of the methods can only be applied to a single scene. To address these issues, this study proposes a self-supervised meta-transfer learning (SelfMTL) method to improve the generalization ability and adaptability of the model through contrastive representation. First, labeled source data, which contains rich feature information, is utilized to train the global-local spectral contrastive learning (GLSL) module by randomly constructing positive and negative pairs from different land covers for the classification task, aiming to effectively discriminate the similarities and differences between spectra. Then, a small sample (only one target-background pair) fine-tuning is utilized to transfer the pretrained GLSL to different target detection (TD) tasks. Finally, a novel adaptive spatial-spectral enhancement (ASSE) module is proposed, which takes into account the joint learning constraints of spatial and spectral information to obtain the final detection result map. The experimental results on four real hyperspectral images (HSIs) datasets verify the superiority of SelfMTL in comparison to many classical and SOTA HTD methods. The codes are available athttps://github.com/ShissHAN/SelfMTL.

Change detection (CD) is one of the prominent research topics in the fields of Earth science and remote sensing. Recently, an increasing number of deep learning-based CD methods have been developed. Most of the current CD methods require lots of pixel-level labels for supervised learning. However, annotating all the changed pixels in bitemporal images is both challenging and time-consuming. In this work, as a first attempt in the field of CD, we propose a novel CD framework, similarity-awareness weakly supervised CD (SWCD) to achieve accurate CD, which uses weakly supervised learning as an auxiliary task to guide the model in both semi-supervised and supervised learning. In the weakly supervised branch (WSB), we incorporate the concept of similarity and introduce similarity information into the supervised branch to guide pixel-level CD learning, thus enhancing feature continuity. Moreover, large kernel convolution attention is introduced to enhance multiscale feature learning. In the supervised branch, we re-evaluate the approach to multiscale feature aggregation and introduce an adaptive feature module to integrate features from both global and local perspectives. Furthermore, our method can serve as a general framework that is compatible with the existing CD approaches. Experimental results on four CD datasets demonstrate the superior effectiveness and generalization of our proposed method. The code is available athttps://github.com/ZijunTan/SWCD.

Due to the powerful feature extraction capabilities of deep learning, a series of deep learning-based methods for hyperspectral image (HSI) classification have been proposed and achieved satisfactory performance. However, most of these methods require a large number of labeled data, and the collection of completely accurate pixel-level labeled HSI data is difficult, resulting from the intricate label ambiguity of HSI and incomplete prior knowledge of annotators. Simultaneously, a few researchers focus on label ambiguity for HSI classification. Partial label learning (PLL) is one of the strategies to solve the problem where each training instance is assigned a candidate label set, among which only one is the ground truth label, which can essentially alleviate labeling difficulties. In this article, a max-gap loss-guided Siamese reconstruction network (MAGS) is proposed to combine PLL with HSI classification. MAGS consists of three components, including a spatial-spectral encoder, a spatial-spectral decoder, and a Siamese spatial-spectral encoder for high-quality feature representation learning to facilitate label disambiguation. In the encoding process, MAGS introduces the cross-attention and max-matching fusion strategies to obtain more representative features. In addition, to improve label disambiguation, the maximum gap loss is designed to guide the model training. Quantitative and qualitative results indicate that the MAGS outperforms several state-of-the-art methods on three HSI datasets. The code is available athttps://github.com/Nemo96yu/MAGS.

Multitemporal hyperspectral images (HSIs) have been widely applied in change detection (CD) of different land covers for their rich spectral features and image details. However, alignment and labeling pairs of bitemporal HSIs are labor-intensive. In this article, we propose a single-temporal mask-based network (STMNet) for self-supervised HSI CD from a new perspective of detecting masks as changes. STMNet implements self-supervised by treating artificially constructed masks attached to single-temporal HSI as changed regions. To this end, we design a multiscale mask change simulation (MMCS) strategy to generate pseudo-second-temporal HSI closer to the real case. Meanwhile, a global-local feature aggregation network is proposed to enhance long-distance and local spatial-spectral feature extraction. To the best of our knowledge, this is the first work in the field of HSI CD that uses single-temporal HSIs and eliminates the need for labeling and pairing samples, alleviating the problem of difficult multitemporal HSI annotation. The visual and quantitative experimental results on three HSI datasets show that the proposed STMNet outperforms the compared state-of-the-art methods for HSI CD. Codes are available athttps://github.com/Zhoutya/ChangeDetection-STMNet.

Snapshot compressive imaging (SCI) compresses a 3D hyperspectral image (HSI) into a 2D measurement, significantly improving imaging efficiency while preserving the spatial and spectral information inherent in HSI. However, reconstructing high-quality HSIs from compressed measurements remains a core challenge due to the complexity of the inverse problem. Transformer-based methods have recently shown promising performance in HSI reconstruction. Nonetheless, effectively capturing local information, long-range dependencies, and multi-scale features within a reasonable computational cost remains a significant challenge. In this paper, we propose a dual-stage multiscale Transformer (DSMT) tailored for HSI reconstruction, which adopts a coarse-to-fine framework to enhance reconstruction accuracy and network generalization. Specifically, we design a novel U-Net architecture with a dual-branch encoder, where two separate branches process distinct features and are fused to achieve more refined reconstruction results. Full-scale skip connections are introduced to strengthen feature fusion across different stages. To further improve performance, we develop a novel self-attention mechanism called dual-window multiscale multi-head self-attention (DWM-MSA). By utilizing two differently sized windows, DWM-MSA captures long-range dependencies and local information at multiple scales, significantly boosting reconstruction quality. Additionally, we introduce a hybrid positional embedding method, conditional/relative positional embedding (CRPE), which dynamically models both spatial and spectral dependencies, effectively enhancing the Transformer's capacity for HSI reconstruction. Extensive quantitative and qualitative experiments on both the simulated and the real data are conducted to demonstrate the superior performance, stability, and generalization ability of our DSMT. Code of this project is at https://github.com/chenx2000/DSMT.

Automatic road extraction from high-resolution remote sensing images has greatly facilitated the applications of high-precision road mapping in autonomous driving and intelligent transportation. However, challenges such as occlusions from buildings, trees, and complex road shapes bring great difficulties to precise road extraction. Also, existing methods often overlook the integrity of road direction and edge, leading to unsatisfactory extraction results. To alleviate the issue, this paper has presented a direction and edge co-awareness network (DENet). Firstly, the road edge detector (RED) is introduced to extract coarse road edges with abundant directional information. By leveraging the edge enhancement blocks, the edge structures of road can be efficiently refined, achieving the extraction of intricate narrow and elongated road shapes. Secondly, we incorporate the directional spatial attention (DSA) mechanism within the dual encoders and decoders to promote the extraction and fusion of road directional information and elongated features from different orientations, thus greatly mitigating the road occlusion issue. Finally, to fully interlace potential road information, a grouped local-global feature fusion (GLFF) is specifically designed to exchange multi-scale semantic information across different channels, simultaneously emphasizing road features and suppressing irrelevant background features. Numerous experimental results on three public datasets demonstrate the effectiveness and efficiency of the proposed DENet for road extraction, achieving F1 scores of 78.51% on the CHN6-CUG dataset, 79.35% on the Massachusetts road dataset, and 77.90% on the GF2-FC dataset, outperforming several existing state-of-the-art methods. The code is available at:https://github.com/gwy103/DENet.

Coded aperture snapshot spectral imaging (CASSI) system is an effective manner for hyperspectral snapshot compressive imaging. The core issue of CASSI is to solve the inverse problem for the reconstruction of hyperspectral image (HSI). In recent years, Transformer-based methods achieve promising performance in HSI reconstruction. However, capturing both long-range dependencies and local information while ensuring reasonable computational costs remains a challenging problem. In this paper, we propose a Transformer-based HSI reconstruction method called dual-window multiscale Transformer (DWMT), which is a coarse-to-fine process, reconstructing the global properties of HSI with the long-range dependencies. In our method, we propose a novel U-Net architecture using a dual-branch encoder to refine pixel information and full-scale skip connections to fuse different features, enhancing the extraction of fine-grained features. Meanwhile, we design a novel self-attention mechanism called dual-window multiscale multi-head self-attention (DWM-MSA), which utilizes two different-sized windows to compute self-attention, which can capture the long-range dependencies in a local region at different scales to improve the reconstruction performance. We also propose a novel position embedding method for Transformer, named con-abs position embedding (CAPE), which effectively enhances positional information of the HSIs. Extensive experiments on both the simulated and the real data are conducted to demonstrate the superior performance, stability, and generalization ability of our DWMT. Code of this project is at https://github.com/chenx2000/DWMT.

Cross-view consensus representation plays a critical role in hyperspectral images (HSIs) clustering. Recent multi-view contrastive cluster methods utilize contrastive loss to extract contextual consensus representation. However, these methods have a fatal flaw: contrastive learning may treat similar heterogeneous views as positive sample pairs and dissimilar homogeneous views as negative sample pairs. At the same time, the data representation via self-supervised contrastive loss is not specifically designed for clustering. Thus, to tackle this challenge, we propose a novel multi-view clustering method, i.e., Enhanced Multi-View Contrastive Clustering (EMVCC). First, the spatial multi-view is designed to learn the diverse features for contrastive clustering, and the globally relevant information of spectrum-view is extracted by Transformer, enhancing the spatial multi-view differences between neighboring samples. Then, a joint self-supervised loss is designed to constrain the consensus representation from different perspectives to efficiently avoid false negative pairs. Specifically, to preserve the diversity of multi-view information, the features are enhanced by using probabilistic contrastive loss, and the data is projected into a semantic representation space, ensuring that the similar samples in this space are closer in distance. Finally, we design a novel clustering loss that aligns the view feature representation with high confidence pseudo-labels for promoting the network to learn cluster-friendly features. In the training process, the joint self-supervised loss is used to optimize the cross-view features.Abundant experiment studies on numerous benchmarks verify the superiority of EMVCC in comparison to some state-of-the-art clustering methods. The codes are available at https://github.com/YiLiu1999/EMVCC.

Hyperspectral anomaly detection (HAD) aims to label each hyperspectral image (HSI) pixel as background or anomaly, in a totally unsupervised manner. Thus, a fine background representation is vital to obtain good HAD performance. This article introduces background endmember representation and proposes a novel HAD method termed learnable background endmember with subspace representation (LEBSR). First, the HSI is unmixed to simultaneously obtain the background endmembers and their abundances. The three constraints of$p$-norm, sum-to-one, and nonnegativity work together to promote a more meaningful and accurate background endmember representation. In addition, a mapping matrix with orthogonality is jointly optimized to transform the priori backgrounds into the background endmember subspace, and then the mapped priori backgrounds are approximated to the low-rank representation (LRR) with the background endmembers. With the methodology, the backgrounds can be well reconstructed under the guidance of the priori background information to accurately detect anomalous pixels with the reconstructed residuals. The experimental results on several HSI datasets verify the superior performance of LEBSR than the state-of-the-art methods.https://github.com/HalongL/HAD-LEBSR

Infrared Small Target Detection (IRSTD) is a challenging task of identifying small targets with low signal-to-noise ratios in complex backgrounds. Traditional methods in the complex background of IRSTD lead to a large number of false alarms and missdetections. Although CNN-based methods have made progress in IRSTD, how to extract more effective information and fully utilize inter-layer information remains an unresolved issue. Therefore, this paper proposed a Dual-Encoder Multi-stage Feature Fusion Network (DMFNet). Specifically, we designed a dual-encoder with different inputs to capture more effective small target feature information. We then designed a Receptive Field Expansion Attention Module (REAM) to incorporate non-local contextual information. In the decoding phase, the Triple Cross-layer Fusion Module (TCFM) was developed to exchange the low-level spatial details and the high-level semantic information for preserving more small target information in deeper layers. Finally, by concatenating multi-scale features from various layers of the decoder, more discriminative feature maps were generated to clearly describe the infrared small targets. Experimental results on the NUDT-SIRST, NUAA-SIRST, and IRSTD-1k datasets demonstrated that DMFNet outperforms some other state-of-the-art methods, achieving superior detection performance. Codes: https://github.com/BJZHOU2000/DMFNet.

Transformers has gained widespread adoption in remote sensing image (RSI) segmentation. However, RSI has densely overlapping terrain and significant shadow, making it challenging to segment the blended boundaries of terrains that are the hard classes. Currently, most transformer-based methods construct the self-attention with a sliding window, which influences the feature receptive fields to conquer the intersecting and overlapping objects. Additionally, they often rarely focus specifically on the representation of these hard segmentation objects. To overcome these challenges, we propose a novel Enhancing Semantic Mask Segmentation Network (ESMS-Net) framework including a local-global joint encoder, an auxiliary enhanced encoder, and a multiscale dense decoder. In the local-global joint encoder, we construct a Pyramid Pooling AtrousFormer (PPAFormer) that performs the self-attention with a pyramid-structured atrous sliding window, which enhances the range of receptive fields and the global representation performance. Meanwhile, we construct the dual-feature fusion module (DFFM) and multilevel feature weighted fusion (MFWF) in the multiscale dense decoder to reduce information loss and facilitate the interaction of deep semantic information. For the auxiliary enhanced encoder, we develop a semantic mask based on the predicted results to maintain the hard segmentation classes, and then use the same structure as the first two stages of the local-global joint encoder to learn the hard regions again. Extensive experiments demonstrate the proposed ESMS-Net can achieve significant improvements for segmentation performance compared with the state-of-the-art methods on the ISPRS-Vaihingen and Potsdam datasets. The code will be available athttps://github.com/Wzysaber/ESMS-Net.

Due to the lack of labeled information and the high spectral variability in high-dimensional hyperspectral images (HSI), HSI clustering has emerged as an effective unsupervised approach for HSI information extraction and classification. Deep clustering methods have achieved significant success in unsupervised HSI classification (HSIC) and have gained increasing attention. However, these methods have limitations in terms of robustness, adaptability, and feature representation when dealing with complex large-scale HSI datasets. Therefore, this paper introduced a novel Self-supervised Double-Structure Transformer (SDST) approach for hyperspectral image clustering. Specifically, in our approach, we designed a shared Autoformer structure based on autoencoder to learn the global properties of HSI data by fusing the multi-level features from autoencoder with Transformer. Furthermore, we proposed a siamese Dual-Former Graph Module with superpixel-level features for fewer nodes, which reveals long-dependency graph convolutional features, resulting in more precise graph structure features. By constructing graph with long-dependencies, this module significantly preserves the properties of global dependencies, while focusing on the local features of each superpixel to better represent the fine-grained local details. Finally, we designed a Joint Optimization Module to jointly optimize the double-structure model composed of the shared Autoformer Module and the siamese Dual-Former Graph Module. To validate the effectiveness of the proposed SDST method, we conducted a series of experiments on the Salinas, Botswana, Indian Pines, and Houston2013 datasets. The proposed SDST achieves competitive clustering accuracies compared with the state-of-the-art clustering methods. Codes: https://github.com/YiLiu1999/SDST.

Hyperspectral target detection (HTD) has been a critical issue in the field of Earth observation, with widespread applications in both military and civilian domains. However, existing deep learning-based HTD methods are hindered due to insufficient and low-quality prior training samples, as well as inadequate background suppression capabilities. To address these issues, this article proposes an adversarial sample generation-based multiscale Siamese network (AGMS) for HTD. First, the AGMS utilizes the idea of generative adversarial learning based on the prior few targets and diverse backgrounds to generate adversarial target-background sample pairs, thereby producing high-quality training samples, which enhances the distinctiveness between the target and background samples by adversarially training the generator to produce the target/background samples. In addition, to further highlight the targets and suppress the backgrounds, a difference amplification loss and an adaptive weighted binary cross-entropy loss are proposed. Finally, a multiscale convolutional Siamese network model is designed to explore the generated spectral information at multiple levels and achieve target detection through contrastive learning. Numerous experimental results on four real HSI datasets verify the superiority of the AGMS in comparison to many classical and recently proposed HTD methods. The codes are available athttps://github.com/ShissHAN/AGMS.

Multi-temporal Hyperspectral images (HSIs) have wide applications in change detection (CD) of different land-covers for their rich spectral features and image details. Traditional supervised learning-based HSI CD algorithms often rely on a substantial number of labeled samples. However, it requires a significant cost in sample annotation. In this paper, we propose a diff-feature contrast enhancement network (DCENet) for semi-supervised HSI CD, which leverages a limited number of labeled samples to guide the training process and a large number of unlabeled samples to improve the confidence of change detection. To achieve this, a differential fusion attention (DFA) sub-network is constructed to extract temporal features from the initial input HSI patches. The dual-branch siamese enhancement module (SEM) is utilized to enhance the generalization of differential features in the feature maps. Herein, multi-scale Kullback-Leibler divergence and feature-enhanced probabilistic contrast loss are designed to constrain the SEM. The proposed method excels at detecting subtle changes in bi-temporal HSIs simultaneously improving the generalization performance of networks. The visual and quantitative experimental results on four HSI datasets show that the proposed DCENet outperforms the compared state-of-the-art methods for HSI CD. Codes: https://github.com/Zhoutya/ChangeDetection-DCENet.

Semi-supervised learning (SSL) plays a crucial role in airborne laser scanning (ALS) point cloud semantic segmentation to reduce the cost of sample labeling. However, the prevailing semi-supervised approaches mainly focus on local and global feature aggregation, which neglects the multiscale and neighborhood structure properties of ALS point clouds. To address this issue, we propose an innovative approach called the Hybrid Structured Constraint Network (HSCN) for semi-supervised semantic segmentation of ALS point clouds. HSCN makes full use of a large number of unlabeled samples to guide the model training under limited labeled samples. To process the unlabeled samples, we construct a global awareness loss (GAL) to constrain the global distribution of point clouds. Then, we also design a multiscale geometric structure similarity metric loss (MLS) to align the neighborhood structure for point clouds at different scales. In addition, we utilize the multiscale features to develop a multilevel fused pseudo-label generator for obtaining high-value pseudo-labels, and then a pseudo-label loss (PLS) is constructed to reduce the class mean probability discrepancies. The proposed HSCN fully utilizes the multiscale and neighborhood structure properties of unlabeled samples to achieve a robust model under limited labeled samples. Extensive experimental analysis using three benchmark datasets (i.e., ISPRS, LASDU, and DFC2019) reveals that our proposed method achieves comparable advantages to some existing advanced fully supervised approaches, even only 0.1% labeled samples for model training. The code is available athttps://github.com/SC-shendazt/HSCN.

Semantic segmentation of airborne laser scanning (ALS) point clouds using deep learning is a hot research in remote sensing and photogrammetry. A current trend is to aggregate contextual features from different scales for boosting network generalization and diversity discrimination capabilities. One main challenge is how to achieve effective fusion with multiscale information. In this letter, we propose a muti-level context feature fusion network (MCFN) for semantic segmentation of ALS point cloud based on an encoder-decoder structure. More specifically, we design the squeeze-expansion shared MLP module (SE-MLP) following kernel point convolution (KPConv) in the encoding stage, which can extend the receptive field of KPConv. To aggregate low-level features and high-level representations, we establish channel self-attention between skip connections. In the decoding stage, we develop a cross-layer attention fusion module (CAF) to generate additional discriminative channel features by fusing multi-scale features at different upsampling layers. Experiments on the ISPRS and LASDU datasets demonstrate the superiority of the proposed method. Code: https://github.com/SC-shendazt/MCFN.

Hyperspectral image (HSI) anomaly detection (AD) generally considers background pixels as low-rank distribution and anomaly pixels as sparse distribution. However, it is usually difficult to construct an accurate background dictionary for the background pixels composed of different land-covers, and completely separate sparse anomaly targets from various complicated background pixels with complex mixed noise interference. To address these challenges, we propose an anti-noise hierarchical mutual-incoherence-induced discriminative learning (AHMID) method for AD of HSI. A structural incoherence constraint is designed to constrain the inherent dissimilarity and incoherence between background and anomalies for improving their separability. Then, a first-order statistic constraint is conducted on targets to enhance the anomaly representation, and a decentralization constraint is used on background to suppress the background representation. Meanwhile, a mixed noise model is constructed by ℓ1,1-norm and Frobenius norm to improve the anti-noise performance. Finally, a hierarchical alternating strategy is developed to gradually optimize the background and anomalies. Experiments on six HSI AD datasets show that the proposed method outperforms a few state-of-the-art AD algorithms. Code: https://github.com/HalongL/HAD-AHMID.

For hyperspectral image (HSI) classification, two branch networks generally use the convolution neural networks (CNNs) to extract the spatial features and the long short-term memory (LSTM) to learn the spectral features. However, CNN with a local kernel neglects the global properties of the whole HSI. LSTM doesn’t consider the macroscopic and detailed information of spectra. In this paper, we propose a dual-view spectral and global spatial feature fusion network (DSGSF) to extract the spatial-spectral features for HSI classification, including a spatial subnetwork and a spectral subnetwork. In the spatial subnetwork, we propose a global spatial feature representation model based on the encoder-decoder structure with channel attention and spatial attention to learn the global spatial features. In the spectral subnetwork, we design a dual-view spectral feature aggregation model with view attention to learn the diversity of spectral features. By fusing the two subnetworks, we construct DSGSF to extract the spatial-spectral features of HSI with strong discriminating performance. Experimental results on three public datasets illustrate that the proposed method can achieve competitive results compared with the state-of-the-art methods. Code: https://github.com/RZWang-WH/DSGSF.

For hyperspectral image (HSI) change detection (CD), multiscale features are usually used to construct the detection models. However, the existing studies only consider the multiscale features containing changed and unchanged components, which is difficult to represent the subtle changes between bitemporal HSIs in each scale. To address this problem, we propose a multiscale diff-changed feature fusion network (MSDFFN) for HSI CD, which improves the ability of feature representation by learning the refined change components between bitemporal HSIs under different scales. In this network, a temporal feature encoder–decoder subnetwork, which combines a reduced inception (RI) module and a cross-layer attention module to highlight the significant features, is designed to extract the temporal features of HSIs. A bidirectional diff-changed feature representation (BDFR) module is proposed to learn the fine changed features of bitemporal HSIs at various scales to enhance the discriminative performance of the subtle change. A multiscale attention fusion (MSAF) module is developed to adaptively fuse the changed features of various scales. The proposed method can not only discover the subtle change in bitemporal HSIs but also improve the discriminating power for HSI CD. Experimental results on three HSI datasets show that MSDFFN outperforms a few state-of-the-art methods.

Kernel point convolution (KPConv) can effectively represent the point features of point cloud data. However, KPConv-based methods just consider the local information of each point, which is very difficult to characterize the intrinsic properties of ALS point clouds for complex laser scanning conditions. Therefore, we rethink KPConv and propose a recurrent residual dual attention network (RRDAN) based on the encoder-decoder structure for the semantic segmentation of ALS point cloud data. In the encoder stage, we design an attention kernel point convolution (AKPConv) block by using a scaling factor of batch normalization to highlight the significant channel information. Then, we use the AKPConv block to develop a recurrent residual kernel attention (RRKA) module to iteratively aggregate the local neighborhood features. In the decoder stage, we design a global and local channel attention (GLCA) module with global connection and local 1D convolution to interact the global and local information after fusing the upsampled high-level representations and the skip-connected low-level features. In addition, to reduce the influence of the long-tail distribution of reflection intensity, we apply gamma transformation to correct the data as normal distribution. The proposed RRDAN can achieve diversified feature aggregation to implement the refined semantic segmentation of ALS point clouds. We evaluate our method on two ALS datasets (i.e., ISPRS and DCF2019) to demonstrate its performance compared to a few advanced methods. Code: https://github.com/SC-shendazt/RRDAN.

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization.

Crowd simulation is a convenient method to evaluate pedestrians’ status and their corresponding management strategies in large public spaces. However, the performance of real-time simulation can be limited by the model’s large-scale computational cost. In order to overcome this difficulty, this study proposes PaCS (Parallel Computation for Crowd Simulation), a parallel computation framework for field-based crowd simulation, based on an enhanced status update method and an efficient task assignment strategy. Parallel computing is introduced with synchronous updates, task division and multiprocessing calculation mechanisms. The movement model is split into the smallest and independent computational units. The field model for simulating the crowd movement has also been improved in terms of weighted multi-direction choice and multi-field environment division. The experiments confirmed that the parallel synchronous algorithm has a significant advantage at the computational scale of more than 10,000 pedestrians. The speedup ratio of the parallel approach can be more than 5 times when simulating one million pedestrians. This framework can help to establish the essential methods for multi-modal transportation systems that require fast simulations for a large-scale crowd. It would also help future digital twin systems to evaluate and validate any potential management strategies when applied in metro stations, railway stations, and other transportation hubs.

Deep convolutional neural networks (CNNs) have made great progress in remote sensing (RS) image scene classification. However, by visualizing the learned feature maps, we find that the popular CNN of ResNet can capture incomplete and inaccurate semantic information for classifying scene images with complex spatial distributions and varying object scales. In this letter, we propose a multilayer and multiattention fusion network (M2FN) to alleviate this issue. Specifically, we first introduce a multilayer adaptive feature fusion (MLAFF) module to model the information interaction between different layers and enhance the network’s multiscale representation ability. Then, we design a multidimensional attention (MA) module to weight the multilayer fused features by comprehensively considering their interdependencies between all possible dimensions. The proposed MA module extends the traditional spatial and channel attentions to a more comprehensive one. Experiments on two benchmark data sets demonstrate the superiority of M2FN for RS scene classification over many state-of-the-art methods.

5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while device-to-device (D2D) communication is a promising paradigm for future 5G networks. To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this article presents an intelligent trust cloud management method. First, an active training mechanism is proposed to construct the standard trust clouds. Second, individual trust clouds of the IoMT devices can be established through fuzzy trust inferring and recommending. Third, a trust classification scheme is proposed to determine whether an IoMT device is malicious. Finally, a trust cloud update mechanism is presented to make the proposed trust management method adaptive and intelligent under an open wireless medium. Simulation results demonstrate that the proposed method can effectively address the trust uncertainty issue and improve the detection accuracy of malicious devices.

A non-orthogonal multiple access (NOMA)-enabled double-layer airborne access vehicular ad hoc networks (DLAA-VANETs) architecture is designed in this paper, which consists of a high-altitude platform (HAP), multiple unmanned aerial vehicles (UAVs) and vehicles. For the designed DLAA-VANETs, we investigate the UAV deployment and network optimization problems. In particular, a UAV deployment scheme based on particle swarm optimization is presented. Then, the NOMA technique is introduced into the designed architecture, which can improve the transmission rate. Afterward, we take the information security into account and formulate a downlink total transmission rate maximization problem by optimizing UAV height and subcarrier allocation. For tackling this non-convex problem, we decouple this downlink total transmission rate maximization problem as two subproblems, where UAV height and subcarrier allocation problems are solved in turn. Moreover, the transmission performance of the designed DLAA-VANETs is analyzed, based on which the security outage probability (SOP) is derived. Finally, simulation results demonstrate that the presented UAV deployment scheme can maximize the relay coverage ratio. In addition, the proposed can achieve a higher downlink total transmission rate in comparison with the current works.

Security is one of the major concerns in industrial wireless sensor networks (IWSNs). To assure the security in clustered IWSNs, this article presents a secure clustering protocol with fuzzy trust evaluation and outlier detection. First, to deal with the transmission uncertainty in an open wireless medium, an interval type-2 fuzzy logic controller is adopted to estimate the trusts. And then, a density-based outlier detection mechanism is introduced to acquire an adaptive trust threshold used to isolate the malicious nodes from being cluster heads. Finally, a fuzzy-based cluster heads election method is proposed to achieve a balance between energy saving and security assurance, so that a normal sensor node with more residual energy or less confidence on other nodes has higher probability to be the cluster head. Extensive experiments verify that our secure clustering protocol can effectively defend the network against attacks from internal malicious or compromised nodes.

With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened. As a result, recommender systems in IoT-based social media need to be developed oriented to groups of users rather than individual users. However, existing methods were highly dependent on explicit preference feedbacks, ignoring scenarios of implicit feedbacks. To remedy such gap, this paper proposes an implicit feedback-based group recommender system using probabilistic inference and non-cooperative game (GREPING) for IoT-based social media. Particularly, unknown process variables can be estimated from observable implicit feedbacks via Bayesian posterior probability inference. In addition, the globally optimal recommendation results can be calculated with the aid of non-cooperative game. Two groups of experiments are conducted to assess the GREPING from two aspects: efficiency and robustness. Experimental results show obvious promotion and considerable stability of the GREPING compared to baseline methods.

Representation-based target detectors for hyperspectral imagery (HSI) have recently aroused a lot of interests. However, existing methods ignore the dictionary structure and cannot guarantee an informative and discriminative representation of test pixels for target detection. To alleviate the problem, this letter proposes a novel sparse and dense hybrid representation-based target detector (SDRD). The proposed detector adopts the idea that the relationship between the background and the target sub-dictionaries is a collaborative competition. The structure of the dictionary is discovered and preserved by learning a sparse and dense hybrid representation for test pixel. Benefitting from this, a compact and discriminative representation can be obtained to better represent the test pixel for an improved detection performance. Experimental results on several HSI data sets verify the effectiveness of SDRD in comparison with several state-of-the-art methods.

Hyperspectral image (HSI) contains complex multiple structures. Therefore, the key problem analyzing the intrinsic properties of an HSI is how to represent the structure relationships of the HSI effectively. Hypergraph is very effective to describe the intrinsic relationships of the HSI. In general, Euclidean distance is adopted to construct the hypergraph. However, this method cannot effectively represent the structure properties of high-dimensional data. To address this problem, we propose a sparse-adaptive hypergraph discriminant analysis (SAHDA) method to obtain the embedding features of the HSI in this letter. SAHDA uses the sparse representation to reveal the structure relationships of the HSI adaptively. Then, an adaptive hypergraph is constructed by using the intraclass sparse coefficients. Finally, we develop an adaptive dimensionality reduction mode to calculate the weights of the hyperedges and the projection matrix. SAHDA can adaptively reveal the intrinsic properties of the HSI and enhance the performance of the embedding features. Some experiments on the Washington DC Mall hyperspectral data set demonstrate the effectiveness of the proposed SAHDA method, and SAHDA achieves better classification accuracies than the traditional graph learning methods.