In recent years, autoencoders and their variants have emerged as effective tools for hyperspectral anomaly detection. Nevertheless, owing to the complex distribution of anomalous regions and the similarity in spatial-spectral features, these models often reconstruct anomalies and backgrounds simultaneously, hindering their ability to distinguish between them and reducing detection accuracy. To address this issue, we propose a novel hyperspectral anomaly detection method based on a spatial-spectral joint mask variational autoencoder (VAE). By combining the probabilistic modeling capabilities of VAEs with a masking-based attention mechanism, our method enables more precise extraction of essential background information in localized regions. Specifically, the spatial-spectral joint masking technique is proposed to guide the network to concentrate on background features across multiple dimensions, tackling issues of spatial structure approximation and spectral redundancy. To further enhance robustness in noisy and complex environments, we iteratively refine the reconstructed residual image through recursive filtering. Extensive comparative experiments and ablation studies on multiple public datasets demonstrate that our approach consistently outperforms existing methods in detection accuracy.

Few-Shot Segmentation (FSS) in remote sensing aims to achieve segmentation of novel categories in query images using limited annotated support images. Despite extensive research, the significant intra-class differences of remote sensing targets continue to hinder progress in this field. Pre-trained vision-language models (VLMs) possess strong generalization capabilities, and their cross-modal information can effectively mitigate intra-class variance issues. However, VLMs rarely focus on dense prediction tasks, and the complexity of remote sensing imagery limits the effectiveness of existing attempts on FSS tasks. To address this issue, this article proposes an Implicit CLIP Prior Decoupling Network (ICPD-Net), which mines effective cross-modal priors from VLMs and leverages ranking information to improve visual metric strategies. Specifically, the Implicit Prior Decoupling Module (IPDM) utilizes ambiguous foreground-background vision-language similarities to construct class-agnostic prompts, while employing a prior learner to mine implicit vision-language priors that alleviate intra-class differences. To fully leverage cross-modal information, the Reliable Feature Fusion Module (RFFM) utilizes vision-language priors to obtain high-confidence query features for fusion with support features, and further mitigating intra-class differences through self-support paradigm. Finally, the Dual Visual Priors Module (DVPM) introduces a novel rank information prior for visual feature measurement. This approach constructs an effective metric learning method by combining the ranking relationships of Euclidean distances between support-query features with the Normalized Discounted Cumulative Gain (NDCG) algorithm, while comprehensively exploring visual metric relationships through traditional cosine similarity prior. Extensive experiments on iSAID-5iand DLRSD-5idemonstrate that our method achieves significant improvements. Particularly under the 1-shot setting, our approach shows exceptional effectiveness, outperforming state-of-the-art methods by up to 11.48% on the iSAID-5i dataset. The code of ICPD-Net is available at https://github.com/yeh15/ICPD-Net.

In recent years, autoencoder-based models have demonstrated significant potential in hyperspectral anomaly detection. However, the inherent class imbalance between anomaly and background samples, coupled with substantial intra-class heterogeneity within background components, often leads traditional models to excessively capture majority-class background features, thereby weakening their discriminative capabilities for minority-class backgrounds and anomaly targets. Concurrently, conventional methods fail to sufficiently model nonlinear features and adapt to complex environments, undermining their detection performance and robustness in intricate environmental interference. To address these challenges, we propose MiKA-HAD, a Minority-augmented KAN-based Hybrid self-supervised framework for hyperspectral Anomaly Detection. It incorporates a density-based clustering-guided sample balancing strategy that dynamically synthesizes minority background samples with spectral-spatial diversity through self-supervised learning. This approach achieves feature space rebalancing while effectively suppressing noise interference on anomaly boundaries. To overcome high-dimensional data complexity and nonlinear feature extraction challenges, we construct a Hybrid Self-Supervised network with a KAN-based multi-path collaborative module that orchestrates synergy between local sensitivity preservation, nonlinear feature modeling, and global consistency maintenance. This tripartite architecture establishes dynamic equilibrium that enhances representation capability and environmental robustness. Extensive experiments show MiKA-HAD achieves superior detection accuracy and stability compared to existing approaches, particularly in complex environments with varying noise conditions. The framework establishes a new paradigm for robust hyperspectral anomaly detection by addressing both class imbalance bias and nonlinear feature modeling limitations.

Remote sensing change detection (RSCD) plays a crucial role in applications such as environmental monitoring and urban planning. With the emergence of foundational vision-language models like CLIP, there is growing interest in integrating textual information into vision tasks. However, in the RSCD domain, limited efforts have been made to effectively leverage textual cues, and challenges persist in capturing differential features. To address these issues, this study proposes the Multimodal Difference Augmentation learning for remote sensing change detection model (MdaCD) that fully exploits textual information and enhances differential feature learning. MdaCD introduces a CLIP-Guided Masking process to direct textual descriptions toward image differences, and a Multimodal Fusion and Difference Augmentation process to integrate and refine differential features across modalities. The CLIP-Guided Masking process applies masking to bi-temporal image pairs before generating text prompts, enabling a more targeted analysis of changes. Meanwhile, the Multimodal Fusion and Difference Augmentation process computes a fused attention map to integrate visual and textual cues, effectively amplifying relevant differences. By applying Difference Augment functions, the differential features from both visual and textual embeddings are further refined and strengthened. The effectiveness of the proposed MdaCD model is validated through extensive experiments on two public RSCD datasets, where it achieves state-of-the-art performance with IoU scores of 84.88% on LEVIR-CD and 71.96% on SYSU-CD. The code and pretrained models of this work will be publicly available at https://github.com/haoyangofficial/MdaCD.

Small object detection in remote sensing faces significant challenges such as their offset-sensitivity caused by the small area coverage, the dim targets in images, and their vulnerability to complex backgrounds, which often result in missed detections and false alarms. In this work, we propose aNoise-Adaptive Context-Aware Detector(NACAD) to alleviate the above problems, which mainly consists of a region proposal network withNoise Adaptive Module(NAM), aContext Aware Module(CAM) and aPosition Refined Module(PRM). The main contributions are threefold: 1) We leverage the information around small objects as positive-incentive noise (also known as π-noise), through enlarging the range of small objects by NAM, more anchors of them are preserved as positive samples, thus stimulating the model to detect small objects. 2) The CAM is designed to provide multiple observation perspectives and abundant contextual representations for the enhancement of object features. 3) To reduce the interference of pure noise in the complicated backgrounds around small objects, the spatial calibration along two coordinate axes is devised by PRM to optimally use information beyond object regions. The effectiveness of our proposed detector, particularly on small objects, has been validated by the experiments on two public datasets, ITCVD and HRRSD. In particular, the NAM improves the recall of small objects, CAM enhances small object features, and PRM helps address the pure noise in complicated backgrounds around small objects.

Recently, rotation detection has gained much attention and shown its potential for accurate localization in remote sensing scenes. However, the objects in remote sensing images have a variety of directions, sizes, and aspect ratios, which makes it difficult to locate and classify objects. Therefore, the object detection task is still facing great challenges in the field of remote sensing. In this paper, we propose a novel single-stage detector, which includes feature alignment block (FAB), double regression branches (DRBs), and a circumcircle rotation box (CRB). FAB utilizes the deformable convolution to flexibly obtain the features of different aspect ratio objects, and aligns the regression features with the corresponding classification features through fusion. Consequently, it can make the extracted feature information have stronger discrimination, which is conducive to improving the object position and classification accuracy. DRBs consist of a main regression branch and an auxiliary regression branch. The main regression branch is used to fine-tune the result of the auxiliary regression branch to obtain a more accurate regression result. Moreover, to eliminate the boundary discontinuity problem faced by regression-based detectors, we construct CRB through designing an angle of rotation and the radius of the circumcircle. Extensive experiments and visual analysis are conducted on three public benchmarks, i.e., DOTA, HRSC2016, and DIOR-R. The results show that our proposed method has excellent localization and classification performance for oriented objects on all the representative datasets.

In the domain of object detection, automatically selecting positive and negative samples methods have become a hot research topic in recent years. However, most of them focus on improving the sampling process but ignore the relationship between object size and feature map, in which the shallow and deep feature layers can capture small and large size objects well respectively. In this paper, we propose a multi-scale sample selection based on statistical characteristics for object detection. To improve the robustness of the Intersection over Union (IoU) threshold, we design a multi-scale sample selection module (MSSM), which takes full advantage of different feature layers. Besides, we introduce a multi-scale attention module (MSAM) by embedding in the feature pyramid networks (FPN) to improve the efficiency of feature fusion. Experiments on MS COCO dataset demonstrate that our method achieves significant improvement over the state-of-the-art methods.

Rotation detection has become a popular topic in the field of remote sensing in recent years. Although quite a few progress has been made, some challenges still exist in feature alignment and regression accuracy due to large aspect ratio and arbitrary orientations of remote sensing objects, especially for the one-stage detectors. To address these problems, we propose a novel one-stage detector from coarse to fine for rotating objects. To alleviate misalignment problem between regression features and classification features, we construct the Feature Alignment Block (FAB). It can flexibly extract the features of objects with different aspect ratios by the deformable convolution and align the regression features with the corresponding classification features. Moreover, to obtain a more accurate regression estimate, we design the refined regression head (RRH) that can effectively fine-tune the coarse regression position. Experiments on the public DOTA and HRSC2016 datasets demonstrate that our proposed method shows excellent detection performance for rotating objects.

Hyperspectral image (HSI) classification is an important research topic in the field of remote sensing. In addition to discriminative spectral information, spatial information also plays an important part in HSI data. So jointly extracting spectral-spatial features is popular to achieve better classification in most recent research. However, simply directly introducing the spatial information without analyzing its necessity will result in some problems. In some cases, spectra have enough material discrimination ability and spatial feature is indeed unneceseary which will brings additional computational burden and even adversely affect the classification results. In order to address these problems, we propose an adaptive spectral spatial feature extraction framework with early prediction strategy for HSI classification. Our method can not only perform high efficiency but also reduce the potential interference of spatial information to improve classification accuracy. Specifically, it mainly consists of two classification branches and a small gate network which is utilized to adaptively determine the necessity of spatial features. Experimental results on the public HSI datasets demonstrate that our approach obtains better performance in both accuracy and efficiency than the comparative state-of-the-art level methods.

In order to remove the non-uniform blur of images captured from dynamic scenes, many deep learning based methods design deep networks for large receptive fields and strong fitting capabilities, or use multi-scale strategy to deblur image on different scales gradually. Restricted by the fixed structures and parameters, these methods are always huge in model size to handle complex blurs. In this paper, we start from the deblurring deconvolution operation, then design an effective and real-time deblurring network. The main contributions are three folded, 1) we construct a spatially variant deconvolution network using modulated deformable convolutions, which can adjust receptive fields adaptively according to the blur features. 2) our analysis shows the sampling points of deformable convolution can be used to approximate the blur kernel, which can be simplified to bi-directional optical flows. So the position learning of sampling points can be supervised by bi-directional optical flows. 3) we build a light-weighted backbone for image restoration problem, which can balance the calculations and effectiveness well. Experimental results show that the proposed method achieves state-of-the-art deblurring performance, but with less parameters and shorter running time.

Abstract Collaborations are pervasive in current science. Collaborations have been studied and encouraged in many disciplines. However, little is known about how a team really functions from the detailed division of labor within. In this research, we investigate the patterns of scientific collaboration and division of labor within individual scholarly articles by analyzing their co‐contributorship networks. Co‐contributorship networks are constructed by performing the one‐mode projection of the author–task bipartite networks obtained from 138,787 articles published in PLoS journals. Given an article, we define 3 types of contributors: Specialists, Team‐players, and Versatiles. Specialists are those who contribute to all their tasks alone; team‐players are those who contribute to every task with other collaborators; and versatiles are those who do both. We find that team‐players are the majority and they tend to contribute to the 5 most common tasks as expected, such as “data analysis” and “performing experiments.” The specialists and versatiles are more prevalent than expected by our designed 2 null models. Versatiles tend to be senior authors associated with funding and supervision. Specialists are associated with 2 contrasting roles: the supervising role as team leaders or marginal and specialized contributors.

Hyperspectral anomaly detection is playing an important role in remote sensing field. Most conventional detectors based on the Reed-Xiaoli (RX) method assume the background signature obeys a Gaussian distribution. However, it is definitely hard to be satisfied in practice. Moreover, background statistics is susceptible to contamination of anomalies in the processing windows, which may lead to many false alarms and sensitiveness to the size of windows. To solve these problems, a novel sparse dictionary learning hyperspectral anomaly detection method with capped norm constraint is proposed. Contributions are claimed in threefold: 1) requiring no assumptions on the background distribution makes the method more adaptive to different scenes; 2) benefiting from the capped norm our method has a stronger distinctiveness to anomalies; and 3) it also has better adaptability to detect different sizes of anomalies without using the sliding dual window. The extensive experimental results demonstrate the desirable performance of our method.

Hyperspectral anomaly detection (AD) is an important problem in remote sensing field. It can make full use of the spectral differences to discover certain potential interesting regions without any target priors. Traditional Mahalanobis-distance-based anomaly detectors assume the background spectrum distribution conforms to a Gaussian distribution. However, this and other similar distributions may not be satisfied for the real hyperspectral images. Moreover, the background statistics are susceptible to contamination of anomaly targets which will lead to a high false-positive rate. To address these intrinsic problems, this paper proposes a novel AD method based on the graph theory. We first construct a vertex- and edge-weighted graph and then utilize a pixel selection process to locate the anomaly targets. Two contributions are claimed in this paper: 1) no background distributions are required which makes the method more adaptive and 2) both the vertex and edge weights are considered which enables a more accurate detection performance and better robustness to noise. Intensive experiments on the simulated and real hyperspectral images demonstrate that the proposed method outperforms other benchmark competitors. In addition, the robustness of the proposed method has been validated by using various window sizes. This experimental result also demonstrates the valuable characteristic of less computational complexity and less parameter tuning for real applications.