Aug 2026· Remote Sensing· 0 citations· 31 references
TL;DR
This paper proposes a boundary-constrained remote sensing change detection network based on vision foundation models (BCNet), which exhibits superior detection accuracy and edge detail preservation capabilities in complex regions.
Abstract
Limited by the diversity and complexity of real-world scenes, existing remote sensing change detection methods often suffer from insufficient fine-grained semantic understanding and blurred boundaries of change targets. To address these issues, this paper proposes a boundary-constrained remote sensing change detection network based on vision foundation models (BCNet). BCNet employs a differential modeling approach and multi-branch guidance mechanism to design a differential detail enhancement module, amplifying fine-grained semantic information. Through cross-layer feature alignment, stepwise fusion, and edge-sensitive modeling, it constructs a multi-scale edge enhancement module that enhances perception of minute variations and edge details, fully leveraging the universal semantic representation capabilities of the vision foundation model. In addition, an edge feature constraint mechanism is introduced that applies dual guidance and supervision during the feature fusion and output stages. This mechanism achieves refined delineation of change region boundaries and significantly mitigates the issue of boundary blurring. Experimental results on four mainstream datasets, namely LEVIR-CD, WHU-CD, NJDS and MSRS-CD, demonstrate that BCNet outperforms 13 state-of-the-art methods in terms of key metrics including F1 and IoU. Against the best VFM-based baseline, BCNet obtains F1 score gains of 0.21%, 0.71%, 6.33% and 0.63% on the above four datasets. Specifically, the proposed method exhibits superior detection accuracy and edge detail preservation capabilities in complex regions.
Building extraction from high-resolution remote sensing imagery is critical for urban planning and smart city development, yet it faces challenges such as blurred boundaries, missing fine details, and severe background interference. To address these issues, this study proposes an improved model named GSU-HRNet, which integrates attention mechanisms and boundary refinement strategies on the basis of UHRNet's high-resolution parallel backbone. An enhanced Pyramid Squeeze-and-Excitation (PSE) module is embedded in the lateral feature transmission paths of each hierarchical stage, capturing multi-scale contextual information via adaptive average pooling of multiple sizes to strengthen semantic responses for buildings and suppress background noise. A Gated Bottleneck Convolution (GBC) module is further introduced in the feature fusion stage, adopting a dual-branch structure with gating mechanisms and residual connections to selectively regulate fused features, alleviate redundant feature accumulation, and improve the stability of feature representation. Experiments were conducted on the aerial imagery subset of the WHU Building Dataset (covering 450 km2 in Christchurch with 8,189 512×512 image tiles), which was split into training, validation and test sets at a ratio of 6:1:3. Ablation experiments verify the effectiveness and complementarity of PSE and GBC modules, with the combined model achieving optimal performance. Quantitative comparisons show that GSU-HRNet outperforms classical models like U-Net and PSPNet, reaching an IoU of 89.93% and an F1-score of 94.50%. Qualitative analysis demonstrates that the proposed model yields clearer building boundaries, more complete structural preservation, and reduced false detections and omissions, even in challenging scenarios with complex building structures and shadow interference. The results confirm that GS-UHRNet effectively enhances feature representation and boundary delineation accuracy, and exhibits strong generalization ability across different building extraction datasets, providing a robust solution for automated building extraction from hig-hresolution remote sensing imagery.
Shi He, Shiye Zhang, Xiujuan Liang et al.· GEOINFORMATICS· 0 citations
A Mahalanobis-Angle Boundary Loss (MABL) is proposed that explicitly enhances boundary and shape consistency and is introduced, built upon MABL, a boundary- aware remote sensing segmentation framework with Struc- tural Penalties.
Yuexi Song, Kailai Sun, Zhuoyue Wang et al.· 0 citations
Frequency and Edge-guided SAM (FE-SAM) is proposed, a scalable and efficient framework for RSISS that adaptively decomposes and modulates frequency-domain features based on the input data and designs EGRefiner, which integrates multi-scale edge-enhanced information extracted from the input image.
Feng Gao, Zizhe Pan, Haoting Wang et al.· IEEE Transactions on Geoscie...· 0 citations
Accurate target boundary extraction is essential for high-precision image interpretation in intelligent sensing systems and provides important technical support for electromagnetic imaging, remote sensing, and vision-assisted signal perception applications. Owing to the limited feature representation capability of the original Detection Transformer (DETR) when processing objects at different scales, target boundary extraction accuracy remains insufficient, particularly in scenarios involving fine-grained contour localization. To address this issue, an improved DETR framework based on a multi-scale perception structure is proposed. A multi-scale encoding architecture integrated with a feature pyramid network is employed to capture features at multiple resolutions, enhancing boundary-aware spatial representations through an edge completion mechanism. A scale-aware multi-head attention module is incorporated into the encoder to preserve scale consistency during global feature modeling and reduce semantic drift. Furthermore, a boundary regression head combined with positional embedding jointly exploits spatial and semantic information to improve contour localization, while an IoU-aware loss function optimizes prediction accuracy in overlapping regions. Experimental results demonstrate that the proposed method increases the average IoU from 0.81 to 0.86 and improves target boundary AP from 44.9% to 48.1%. Compared with Deformable-DETR and Elastic-DETR, it also achieves lower boundary error ratios, confirming that the proposed multi-scale perception mechanism effectively enhances high-precision boundary fitting and offers practical value for intelligent visual sensing systems in engineering applications.
In high-resolution remote sensing, change detection aims to identify land-cover transitions between bi-temporal images, yet blurred boundaries, illumination-induced pseudo-changes, and sparse change signals under severe changed-unchanged imbalance remain major obstacles. To address these issues, an edge-aware multi-scale difference network (EMDNet) is proposed with progressive stages for encoding, difference reasoning, and decoding. An edge-aware central-difference stem (ECF-ResNet18) is designed to retain high-frequency boundary cues before downsampling, helping alleviate early edge attenuation that blurs boundaries and misses small objects. A multi-scale spatiotemporal transformer module (MSST-Module) is introduced to jointly reason over connection, residual, and appearance streams for spectral-spatial-temporal difference modeling, helping separate true material changes from illumination-driven pseudo-changes. An adaptive region-aware progressive decoder (ARP-Decoder) is designed with complementary dual gating to recover sparse change responses when unchanged pixels dominate under severe class imbalance. Together, the three stages link edge-preserving encoding, difference reasoning, and region-aware decoding so that each prepares the representations required by the next. Compared with recent CNN-, transformer-, and Mamba-based methods on LEVIR-CD, SYSU-CD, and WHU-CD, EMDNet achieves F1 scores of 91.09%, 83.01%, and 90.70% and OA of 99.10%, 92.37%, and 99.31%, with a practical inference cost of 13.14 ms per 256×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}256 patch. These results demonstrate that EMDNet improves boundary fidelity over recent strong baselines. Pseudo-change control and sparse-change recovery are also strengthened, while inference cost remains practical.
Ying Liu, Haoyu Jiang, Xinying Zhong et al.· Journal of King Saud Univers...· 0 citations
Object detection in optical remote sensing imagery is severely affected by adverse weather conditions, such as fog and haze, which degrade image quality and obscure structural details. Although recent Transformer-based detectors have achieved promising performance, they suffer from quadratic computational complexity for high-resolution inputs and tend to produce imprecise object boundaries in degraded scenes. To address these issues, we propose an Edge-Guided State-Space DETR (ES-DETR), an end-to-end detection framework that integrates linear-complexity state-space modeling with structural priors. Specifically, a Laplacian Edge-Aware Module (LEM) is designed to extract high-frequency boundary information from foggy images. Moreover, a Structural-Prior-Driven Mamba Fusion Module (SMF) is introduced to incorporate edge-derived structural priors into the Mamba architecture for long-range dependency modeling and feature fusion. This design effectively restores degraded semantic representations. Extensive experiments on foggy remote sensing benchmarks demonstrate that the proposed ES-DETR outperforms state-of-the-art detectors while maintaining a favorable accuracy–efficiency trade-off.
Xiaopeng Yang, Qiang Zhang, Zheng Liang et al.· IEEE Signal Processing Lette...· 0 citations
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