RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch Fusion
Mainstream deep learning segmentation models are designed for small-sized images, and when applied to high-resolution remote sensing images, the limited information contained in small-sized images greatly restricts a model’s ability to capture complex contextual information at a global scale. To mit...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2158 |
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| author | Shiyan Pang Weimin Zeng Yepeng Shi Zhiqi Zuo Kejiang Xiao Yujun Wu |
| author_facet | Shiyan Pang Weimin Zeng Yepeng Shi Zhiqi Zuo Kejiang Xiao Yujun Wu |
| author_sort | Shiyan Pang |
| collection | DOAJ |
| description | Mainstream deep learning segmentation models are designed for small-sized images, and when applied to high-resolution remote sensing images, the limited information contained in small-sized images greatly restricts a model’s ability to capture complex contextual information at a global scale. To mitigate this challenge, we present RPFusionNet, a novel parallel semantic segmentation framework that is specifically designed to efficiently integrate both local and global features. RPFusionNet leverages two distinct feature representations: REGION (representing large areas) and PATCH (representing smaller regions). This framework comprises two parallel branches: the REGION branch initially downsamples the entire image, then extracts features via a convolutional neural network (CNN)-based encoder, and subsequently captures multi-level information using pooled kernels of varying sizes. This design enables the model to adapt effectively to objects of different scales. In contrast, the PATCH branch utilizes a pixel-level feature extractor to enrich the high-dimensional features of the local region, thereby enhancing the representation of fine-grained details. To model the semantic correlation between the two branches, we have developed the Region–Patch scale fusion module. This module ensures that the network can comprehend a wider range of image contexts while preserving local details, thus bridging the gap between regional and local information. Extensive experiments were conducted on three public datasets: WBDS, AIDS, and Vaihingen. Compared to other state-of-the-art methods, our network achieved the highest accuracy on all three datasets, with an IoU score of 92.08% on the WBDS dataset, 89.99% on the AIDS dataset, and 88.44% on the Vaihingen dataset. |
| format | Article |
| id | doaj-art-71f88a46ea9346f386eb03908ba5baa2 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-71f88a46ea9346f386eb03908ba5baa22025-08-20T03:50:20ZengMDPI AGRemote Sensing2072-42922025-06-011713215810.3390/rs17132158RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch FusionShiyan Pang0Weimin Zeng1Yepeng Shi2Zhiqi Zuo3Kejiang Xiao4Yujun Wu5Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, ChinaFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, ChinaFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaMainstream deep learning segmentation models are designed for small-sized images, and when applied to high-resolution remote sensing images, the limited information contained in small-sized images greatly restricts a model’s ability to capture complex contextual information at a global scale. To mitigate this challenge, we present RPFusionNet, a novel parallel semantic segmentation framework that is specifically designed to efficiently integrate both local and global features. RPFusionNet leverages two distinct feature representations: REGION (representing large areas) and PATCH (representing smaller regions). This framework comprises two parallel branches: the REGION branch initially downsamples the entire image, then extracts features via a convolutional neural network (CNN)-based encoder, and subsequently captures multi-level information using pooled kernels of varying sizes. This design enables the model to adapt effectively to objects of different scales. In contrast, the PATCH branch utilizes a pixel-level feature extractor to enrich the high-dimensional features of the local region, thereby enhancing the representation of fine-grained details. To model the semantic correlation between the two branches, we have developed the Region–Patch scale fusion module. This module ensures that the network can comprehend a wider range of image contexts while preserving local details, thus bridging the gap between regional and local information. Extensive experiments were conducted on three public datasets: WBDS, AIDS, and Vaihingen. Compared to other state-of-the-art methods, our network achieved the highest accuracy on all three datasets, with an IoU score of 92.08% on the WBDS dataset, 89.99% on the AIDS dataset, and 88.44% on the Vaihingen dataset.https://www.mdpi.com/2072-4292/17/13/2158RPFusionNetremote sensinglarge-scale remote sensing imagesREGIONPATCH |
| spellingShingle | Shiyan Pang Weimin Zeng Yepeng Shi Zhiqi Zuo Kejiang Xiao Yujun Wu RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch Fusion Remote Sensing RPFusionNet remote sensing large-scale remote sensing images REGION PATCH |
| title | RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch Fusion |
| title_full | RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch Fusion |
| title_fullStr | RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch Fusion |
| title_full_unstemmed | RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch Fusion |
| title_short | RPFusionNet: An Efficient Semantic Segmentation Method for Large-Scale Remote Sensing Images via Parallel Region–Patch Fusion |
| title_sort | rpfusionnet an efficient semantic segmentation method for large scale remote sensing images via parallel region patch fusion |
| topic | RPFusionNet remote sensing large-scale remote sensing images REGION PATCH |
| url | https://www.mdpi.com/2072-4292/17/13/2158 |
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