Building Extraction Network with Gated Mamba-CNN and Wavelet-Based Boundary Enhancement.
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| Title: | Building Extraction Network with Gated Mamba-CNN and Wavelet-Based Boundary Enhancement. |
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| Authors: | Yang, Dongjie1,2 (AUTHOR), Yang, Yuanwei1,2,3 (AUTHOR) 516042@yangtzeu.edu.cn, Gao, Xianjun1,3 (AUTHOR), Huang, Rujing1,4 (AUTHOR), Gao, Xinlong1 (AUTHOR), Han, Kuikui1,2 (AUTHOR), Guo, Kangliang1,3 (AUTHOR), Tao, Yuan4 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1773. 25p. |
| Subjects: | Convolutional neural networks, Wavelet transforms, Image segmentation, Deep learning, Remote sensing, Image enhancement (Imaging systems), Remote-sensing images |
| Abstract: | Highlights: What are the main findings? GWNet combines gated Mamba-CNN modeling and wavelet-based boundary enhancement to improve building extraction by jointly strengthening global-local feature representation and boundary recovery. GWNet achieves the best overall performance on the WHU, Massachusetts, and WHU Satellite I datasets, while ablation results show that GMC mainly improves region completeness and WBO mainly enhances contour quality. What are the implications of the main findings? Adaptive global-local feature fusion is effective for reducing errors caused by spectral heterogeneity, shadow occlusion, and complex background interference in remote sensing building extraction. Wavelet-based high-frequency enhancement provides a simple and robust strategy for preserving building boundaries and improving model generalization across scenes with different resolutions and complexities. Building extraction from high-resolution remote sensing imagery remains challenging due to spectral heterogeneity, complex background interference, and incomplete boundary delineation. Thus, we propose GWNet, which integrates gated Mamba-CNN modeling with wavelet-based boundary enhancement. Specifically, a Gated Mamba-CNN Module (GMC) is embedded into the medium- and low-resolution branches to jointly capture local texture features and long-range dependencies. In addition, a channel-wise gating mechanism is introduced to adaptively balance global contextual information and local structural details, thereby alleviating fragmented predictions and internal holes within the same building caused by variations in roof materials, while reducing the misclassification between buildings and background objects such as roads and bare land. Furthermore, a Wavelet Boundary Optimization Module (WBO) is designed to exploit multi-directional high-frequency components extracted by fixed Haar wavelet filters, thereby enhancing the representation of building boundaries and corners. This design effectively mitigates boundary blurring, incomplete contours, and missed detections caused by the loss of high-frequency edge information during downsampling. Extensive experiments on four public datasets, namely WHU, Massachusetts, WHU Satellite I, and Potsdam, demonstrate the effectiveness and robustness of GWNet across diverse spatial resolutions and scene complexities. Specifically, GWNet achieves IoU/BIoU scores of 90.68%/66.88% on the WHU dataset, 73.02%/93.19% on the Massachusetts dataset, 63.86%/83.77% on the WHU Satellite I dataset, and 83.21%/58.96% on the Potsdam dataset, consistently outperforming several competitive methods. Qualitative results further confirm that GWNet produces more complete building regions and sharper, more continuous boundaries. These findings validate the effectiveness of the proposed global–local feature extraction mechanism and wavelet-based boundary enhancement strategy. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? GWNet combines gated Mamba-CNN modeling and wavelet-based boundary enhancement to improve building extraction by jointly strengthening global-local feature representation and boundary recovery. GWNet achieves the best overall performance on the WHU, Massachusetts, and WHU Satellite I datasets, while ablation results show that GMC mainly improves region completeness and WBO mainly enhances contour quality. What are the implications of the main findings? Adaptive global-local feature fusion is effective for reducing errors caused by spectral heterogeneity, shadow occlusion, and complex background interference in remote sensing building extraction. Wavelet-based high-frequency enhancement provides a simple and robust strategy for preserving building boundaries and improving model generalization across scenes with different resolutions and complexities. Building extraction from high-resolution remote sensing imagery remains challenging due to spectral heterogeneity, complex background interference, and incomplete boundary delineation. Thus, we propose GWNet, which integrates gated Mamba-CNN modeling with wavelet-based boundary enhancement. Specifically, a Gated Mamba-CNN Module (GMC) is embedded into the medium- and low-resolution branches to jointly capture local texture features and long-range dependencies. In addition, a channel-wise gating mechanism is introduced to adaptively balance global contextual information and local structural details, thereby alleviating fragmented predictions and internal holes within the same building caused by variations in roof materials, while reducing the misclassification between buildings and background objects such as roads and bare land. Furthermore, a Wavelet Boundary Optimization Module (WBO) is designed to exploit multi-directional high-frequency components extracted by fixed Haar wavelet filters, thereby enhancing the representation of building boundaries and corners. This design effectively mitigates boundary blurring, incomplete contours, and missed detections caused by the loss of high-frequency edge information during downsampling. Extensive experiments on four public datasets, namely WHU, Massachusetts, WHU Satellite I, and Potsdam, demonstrate the effectiveness and robustness of GWNet across diverse spatial resolutions and scene complexities. Specifically, GWNet achieves IoU/BIoU scores of 90.68%/66.88% on the WHU dataset, 73.02%/93.19% on the Massachusetts dataset, 63.86%/83.77% on the WHU Satellite I dataset, and 83.21%/58.96% on the Potsdam dataset, consistently outperforming several competitive methods. Qualitative results further confirm that GWNet produces more complete building regions and sharper, more continuous boundaries. These findings validate the effectiveness of the proposed global–local feature extraction mechanism and wavelet-based boundary enhancement strategy. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111773 |
