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.

Object re-identification (ReID) performs well on standard benchmarks, yet real deployments are often modality-mixed, where queries arise from heterogeneous sensors and conditions. In this setting, modality gaps and environmental shifts can substantially degrade retrieval accuracy, and a single unified model rarely performs uniformly well across modalities. We therefore study instance-adaptive expert routing for ReID, where a router selects the most suitable expert from a heterogeneous toolbox for a given query and a fixed gallery. We propose an end-to-end framework in which a multimodal LLM serves as the routing policy. The router is trained first with semantic preference supervision, where instance-level expert preferences are derived from retrieval outcomes using a reproducible protocol, and then optimized with policy-gradient learning using downstream retrieval rewards. To support continual expansion of the expert set, we further introduce a non-parametric variant that performs expert selection via retrieval-augmented expert descriptors, enabling new tools to be integrated without retraining. Experiments on cross-modal and cross-domain benchmarks demonstrate consistent improvements over unified ReID models and fixed-expert baselines.Our project is publicly available at the GitHub repository.

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.

Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring. However, existing HSI processing methods are predominantly task-specific and scene-dependent, which severely limits their ability to transfer knowledge across tasks and scenes, thereby reducing the practicality in real-world applications. To address these challenges, we present HyperSIGMA, a vision transformer-based foundation model that unifies HSI interpretation across tasks and scenes, scalable to over one billion parameters. To overcome the spectral and spatial redundancy inherent in HSIs, we introduce a novel sparse sampling attention (SSA) mechanism, which effectively promotes the learning of diverse contextual features and serves as the basic block of HyperSIGMA. HyperSIGMA integrates spatial and spectral features using a specially designed spectral enhancement module. In addition, we construct a large-scale hyperspectral dataset, HyperGlobal-450K, for pre-training, which contains about 450 K hyperspectral images, significantly surpassing existing datasets in scale. Extensive experiments on various high-level and low-level HSI tasks demonstrate HyperSIGMA's versatility and superior representational capability compared to current state-of-the-art methods. Moreover, HyperSIGMA shows significant advantages in scalability, robustness, cross-modal transferring capability, real-world applicability, and computational efficiency.

Deep learning has shown great potential in hyperspectral image (HSI) classification. However, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSIs is laborious and time-consuming, developing algorithms that can yield good performance in a small sample size situation is of great significance. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. However, prevalent solutions are unsatisfactory in feature discrimination and model overfitting, which greatly limits their performance. To remedy these drawbacks, we propose a novel spatial-spectral enhancement and fusion network for hyperspectral image classification with few labeled samples, named SSEFN. Specifically, we design a spatial-spectral enhancement strategy (SSES) to boost the feature discrimination from spatial and spectral perspectives, which enables the model to learn more easily with fewer samples. In addition, we propose an adaptive decision fusion (ADF) module to fuse the decisions of all enhanced features. Since each decision prediction may have a different trend of overfitting, combining multiple predictions alleviates the overfitting. Extensive experiments are conducted on four diverse hyperspectral image datasets. The results show that our method significantly outperforms the state-of-the-art approaches on all datasets and demonstrates the effectiveness and superiority of the proposed model for hyperspectral image classification with few labeled samples. Codes are available athttps://github.com/liushuang963/SSEFN.

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.

Hyperspectral image classification plays an important role in the field of remote sensing. Even though we can easily acquire hyperspectral remote sensing images, obtaining a large number of labeled hyperspectral samples remains challenging, especially in high-altitude or uninhabited areas. In this paper, we propose a hyperspectral classification scheme for scenarios with insufficient labeled samples. This scheme is based on a variant of the ResBlock and a non-learned classifier. First, we introduce a new and simplified backbone network for feature extraction. This network primarily consists of an attention-enhanced ResBlock-in-ResBlock module, which utilizes nested residual modules to enhance nonlinear expression and further optimizes the network using channel attention. Building upon this foundation, we address the challenge of achieving optimal classification with limited labeled training samples, a scenario described by the neural collapse theory. To address this, we introduce the Equiangular Tight Frame (ETF) classifier and the dot-regression loss into hyperspectral classification. We conducted extensive comparative experiments using three hyperspectral image datasets. The experimental results demonstrate that our algorithm achieves superior classification accuracy, especially when the training sample size is small, outperforming other state-of-the-art algorithms. Furthermore, our algorithm maintains a low number of parameters and an overall complexity level.

Hyperspectral Image (HSI) classification is a key technique in remote sensing. Despite the increasing availability of high-quality HSI data, obtaining a large number of labeled samples remains challenging in certain cases. Consequently, HSI classification often faces the issue of insufficiently labeled samples. To address this challenge, sample augmentation techniques can be used to generate additional training samples. However, because of the significant differences between hyperspectral images and ordinary natural images, some augmentation techniques are not suitable for hyperspectral classification scenarios. In this paper, considering phenomena such as spectral aliasing in hyperspectral image classification and imaging processes, we propose a novel online augmentation technique for hyperspectral samples. During training, we apply random gains to the center pixel of labeled samples to increase the number of usable samples. Additionally, since augmented samples may still be insufficient, using overly complex networks can lead to overfitting. Therefore, we introduce a hyperspectral image classification network called Attention-enhancing Residual and Spatial-Channel Attention-based network (ReSC-net). In ReSC-net, we observe that the spatial dimension of hyperspectral blocks is much smaller than the channel dimension, and the limited sample size can lead to overfitting when using complex networks. Thus, we propose a channel attention-enhanced residual module to extract low-level features. Furthermore, ReSC-net introduces new spatial-channel attention to further optimize the extracted deep features for better classification. We conduct experiments on four commonly used HSI datasets. The experimental results demonstrate that our algorithm achieves favorable results on multiple HSI classification evaluation metrics.

The synthetic aperture radar (SAR) image is widely used in many remote sensing applications. In order to store and transmit the increasing SAR image data, more efficient compression algorithms are needed. The purpose of this letter is to introduce a new framework for compressing SAR images. First, we propose a novel analysis and synthesis transform based on multi-Resblocks for transforming the original SAR image into a compact latent representation. Then, a Gaussian mixture model (GMM) is used to estimate the latent representation’s distribution. In order to explore the redundancy within the latent representation, the entropy model parameter is estimated by combining the local context, global context, and hyperprior information. In order to evaluate the performance of the proposed algorithm, we conduct experiments on a dataset of SAR images. The results show that the proposed algorithm outperforms JPEG2000 and some state-of-the-art learned image compression schemes in terms of compression performance.

Real-time wireless sensor networks in the industrial settings usually consist of tens of nodes. Seldom do we see a network of over 100 wireless nodes. The smaller size is usually sufficient as a typical plane unit is no larger than a football field size, and the critical data points of interest are not many. We believe, however, there is need for large scale real-time wireless sensor networks. In this paper we shall use WirelessHART as the platform to study and build one. We shall study the possibility, propose the methods to achieve, and test our solutions with experimentations.