Light-weight neural networks for remote sensing (RS) visual analysis must overcome two inherent redundancies: spatial redundancy from vast, homogeneous backgrounds, and channel redundancy, where extreme scale variations render a single feature space inefficient. Existing models, often designed for natural images, fail to address this dual challenge in RS scenarios. To bridge this gap, we propose LWGANet, a light-weight backbone engineered for RS-specific properties. LWGANet introduces two core innovations: a Top-K Global Feature Interaction (TGFI) module that mitigates spatial redundancy by focusing computation on salient regions, and a Light-Weight Grouped Attention (LWGA) module that resolves channel redundancy by partitioning channels into specialized, scale-specific pathways. By synergistically resolving these core inefficiencies, LWGANet achieves a superior trade-off between feature representation quality and computational cost. Extensive experiments on twelve diverse datasets across four major RS tasks—scene classification, oriented object detection, semantic segmentation, and change detection—demonstrate that LWGANet consistently outperforms state-of-the-art light-weight backbones in both accuracy and efficiency. Our work establishes a new, robust baseline for efficient visual analysis in RS images.

Road extraction has been a common and challenging task in the field of remote sensing images. Due to factors such as the high resolution of remote sensing images and the subtle visibility of road features, existing methods often miss certain areas during detection and extraction. These methods struggle to capture contextual information effectively and tend to exhibit false positives and false negatives when handling objects of varying sizes. This article proposes a network based on a multi-scale adaptive decoder and diverse selection (MADSNet) to address the issue of inadequate contextual information capture. By leveraging feature diverse selection, the method minimizes errors in distinguishing between road features and background interference. Specifically, the multi-scale feature flexible extraction (MFFE) decoder utilizes the relevance inquiry attention (RIA) module and scope flexible fusion (SFF) module to enhance the ability to capture contextual information with relatively low computational demands. The optimal choice graph attention (OCGA) module aggregates neighboring nodes with similar features in a graph structure, improving focus on the single class of roads. Furthermore, a multi-level feature selection (MFS) module is proposed to activate the features relevant to the current stage while suppressing features from other stages and interfering with noise. Quantitative and qualitative experimental results on three public datasets demonstrate that the proposed MADSNet outperforms currently popular methods in terms of performance. The code will be available at https://github.com/Talent02/MADSNet.

Remote Sensing Image Dehazing (RSID) poses significant challenges in real-world scenarios due to the complex atmospheric conditions and severe color distortions that degrade image quality. The scarcity of real-world remote sensing hazy image pairs has compelled existing methods to rely primarily on synthetic datasets. However, these methods struggle with real-world applications due to the inherent domain gap between synthetic and real data. To address this, we introduce Real-World Remote Sensing Hazy Image Dataset (RRSHID), the first large-scale dataset featuring real-world hazy and hazy-free image pairs across diverse atmospheric conditions. Based on this, we propose MCAF-Net, a novel framework tailored for real-world RSID. Its effectiveness arises from three innovative components: Multi-branch Feature Integration Block Aggregator (MFIBA), which enables robust feature extraction through cascaded integration blocks and parallel multi-branch processing; Color-Calibrated Self-Supervised Attention Module (CSAM), which mitigates complex color distortions via self-supervised learning and attention-guided refinement; and Multi-Scale Feature Adaptive Fusion Module (MFAFM), which integrates features effectively while preserving local details and global context. Extensive experiments validate that MCAF-Net demonstrates state-of-the-art performance in real-world RSID, while maintaining competitive performance on synthetic datasets. The introduction of RRSHID and MCAF-Net sets new benchmarks for real-world RSID research, advancing practical solutions for this complex task. The code and dataset are publicly available at here.

In the realm of computer vision (CV), balancing speed and accuracy remains a significant challenge. Recent efforts have focused on developing lightweight networks that optimize computational efficiency and feature extraction. However, in remote sensing (RS) imagery, where small and multiscale object detection is critical, these networks often fall short in performance. To address these challenges, DecoupleNet is proposed, an innovative lightweight backbone network specifically designed for RS visual tasks in resource-constrained environments. DecoupleNet incorporates two key modules: the feature integration downsampling (FID) module and the multibranch feature decoupling (MBFD) module. The FID module preserves small object features during downsampling, while the MBFD module enhances small and multiscale object feature representation through a novel decoupling approach. Comprehensive evaluations on three RS visual tasks demonstrate DecoupleNet’s superior balance of accuracy and computational efficiency compared to existing lightweight networks. On the NWPU-RESISC45 classification dataset, DecoupleNet achieves a top-1 accuracy of 95.30%, surpassing FasterNet by 2%, with fewer parameters and lower computational overhead. In object detection tasks using the DOTA 1.0 test set, DecoupleNet records an accuracy of 78.04%, outperforming ARC-R50 by 0.69%. For semantic segmentation on the LoveDA test set, DecoupleNet achieves 53.1% accuracy, surpassing UnetFormer by 0.70%. These findings open new avenues for advancing RS image analysis on resource-constrained devices, addressing a pivotal gap in the field. The code and pretrained models are publicly available athttps://github.com/lwCVer/DecoupleNet.

In the study of remote sensing images, the problem of change detection (CD) is crucial. Convolutional neural networks (CNNs) are well-liked feature extraction structures that are frequently used in CD. On the other hand, graph convolutional networks (GCNs) are effective in building contextual structure information. Compared with CNN, GCN can make full use of the graph structure information to capture the changing features between different areas in the graph by learning the connections and interactions between nodes. In contrast, traditional pixel-based CNNs may have difficulty modeling semantic relationships and temporal variations among features and are susceptible to noise interference. So in this article, we extract optimization information using a GCN structure. Due to the particularity of remote sensing images, edge information is often ignored, which is useful in the field of CD. In this article, we propose an attention-aware Sobel GCN (ASGCN) for remote sensing image CD. First, we use a Siamese CNN to extract primary multilevel features. Then, a dual-branch attention module (DAM) including coordinate attention and multiscale local attention module (MLAM) is proposed to focus on informative pixels, we use Sobel operator to construct graph, and the graph convolutional module can expand receptive field and extract edge information. Attention fusion module (AFM) is adopted at decoder to perform effective feature fusion. Extensive comparative experiments on three CD datasets, LEVIR-CD, WHU-CD, and DSIFN-CD, verify the effectiveness of the proposed ASGCN.

Remote sensing (RS) images present unique challenges for computer vision due to lower resolution, smaller objects, and fewer features. Mainstream backbone networks show promising results for traditional visual tasks. However, they use convolution to reduce feature map dimensionality, which can result in information loss for small objects in RS images and decreased performance. To address this problem, we propose a new and universal downsampling module named Robust Feature Downsampling (RFD). RFD fuses multiple feature maps extracted by different downsampling techniques, creating a more robust feature map with a complementary set of features. Leveraging this, we overcome the limitations of conventional convolutional downsampling, resulting in more accurate and robust analysis of RS images. We develop two versions of RFD module, Shallow RFD (SRFD) and Deep RFD (DRFD), tailored to adapt to different stages of feature capture and improve feature robustness. We replace the downsampling layers of existing mainstream backbones with RFD module and conduct comparative experiments on several public RS image datasets. The results show significant improvements compared to baseline approaches in RS image classification, object detection, and semantic segmentation. Specifically, our RFD module achieved an average performance gain of 1.5% on NWPU-RESISC45 classification dataset without utilizing any additional pretraining data, resulting in state-of-the-art performance on this dataset. Moreover, in detection and segmentation tasks on DOTA and iSAID datasets, our RFD module outperforms the baseline approaches by 2-7% when utilizing pretraining data from NWPU-RESISC45. These results highlight the value of RFD module in enhancing the performance of RS visual tasks.