Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images
High spatial resolution (HSR) remote sensing images have a wide range of application prospects in the fields of urban planning, agricultural planning and military training. Therefore, the research on the semantic segmentation of remote sensing images becomes extremely important. However, large data...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/22/5830 |
| _version_ | 1797464032877215744 |
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| author | Xinran Du Shumeng He Houqun Yang Chunxiao Wang |
| author_facet | Xinran Du Shumeng He Houqun Yang Chunxiao Wang |
| author_sort | Xinran Du |
| collection | DOAJ |
| description | High spatial resolution (HSR) remote sensing images have a wide range of application prospects in the fields of urban planning, agricultural planning and military training. Therefore, the research on the semantic segmentation of remote sensing images becomes extremely important. However, large data volume and the complex background of HSR remote sensing images put great pressure on the algorithm efficiency. Although the pressure on the GPU can be relieved by down-sampling the image or cropping it into small patches for separate processing, the loss of local details or global contextual information can lead to limited segmentation accuracy. In this study, we propose a multi-field context fusion network (MCFNet), which can preserve both global and local information efficiently. The method consists of three modules: a backbone network, a patch selection module (PSM), and a multi-field context fusion module (FM). Specifically, we propose a confidence-based local selection criterion in the PSM, which adaptively selects local locations in the image that are poorly segmented. Subsequently, the FM dynamically aggregates the semantic information of multiple visual fields centered on that local location to enhance the segmentation of these local locations. Since MCFNet only performs segmentation enhancement on local locations in an image, it can improve segmentation accuracy without consuming excessive GPU memory. We implement our method on two high spatial resolution remote sensing image datasets, DeepGlobe and Potsdam, and compare the proposed method with state-of-the-art methods. The results show that the MCFNet method achieves the best balance in terms of segmentation accuracy, memory efficiency, and inference speed. |
| first_indexed | 2024-03-09T18:02:12Z |
| format | Article |
| id | doaj.art-9931549c27144b6485431cc0511a39fc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:02:12Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9931549c27144b6485431cc0511a39fc2023-11-24T09:51:07ZengMDPI AGRemote Sensing2072-42922022-11-011422583010.3390/rs14225830Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing ImagesXinran Du0Shumeng He1Houqun Yang2Chunxiao Wang3College of Computer Science and Technology, Hainan University, Haikou 570100, ChinaCollege of Computer Science and Technology, Hainan University, Haikou 570100, ChinaCollege of Computer Science and Technology, Hainan University, Haikou 570100, ChinaHainan Geomatics Centre of Ministry of Natural Resources, Haikou 570203, ChinaHigh spatial resolution (HSR) remote sensing images have a wide range of application prospects in the fields of urban planning, agricultural planning and military training. Therefore, the research on the semantic segmentation of remote sensing images becomes extremely important. However, large data volume and the complex background of HSR remote sensing images put great pressure on the algorithm efficiency. Although the pressure on the GPU can be relieved by down-sampling the image or cropping it into small patches for separate processing, the loss of local details or global contextual information can lead to limited segmentation accuracy. In this study, we propose a multi-field context fusion network (MCFNet), which can preserve both global and local information efficiently. The method consists of three modules: a backbone network, a patch selection module (PSM), and a multi-field context fusion module (FM). Specifically, we propose a confidence-based local selection criterion in the PSM, which adaptively selects local locations in the image that are poorly segmented. Subsequently, the FM dynamically aggregates the semantic information of multiple visual fields centered on that local location to enhance the segmentation of these local locations. Since MCFNet only performs segmentation enhancement on local locations in an image, it can improve segmentation accuracy without consuming excessive GPU memory. We implement our method on two high spatial resolution remote sensing image datasets, DeepGlobe and Potsdam, and compare the proposed method with state-of-the-art methods. The results show that the MCFNet method achieves the best balance in terms of segmentation accuracy, memory efficiency, and inference speed.https://www.mdpi.com/2072-4292/14/22/5830semantic segmentationhigh spatial resolution remote sensing imagesmemory efficiency |
| spellingShingle | Xinran Du Shumeng He Houqun Yang Chunxiao Wang Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images Remote Sensing semantic segmentation high spatial resolution remote sensing images memory efficiency |
| title | Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images |
| title_full | Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images |
| title_fullStr | Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images |
| title_full_unstemmed | Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images |
| title_short | Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images |
| title_sort | multi field context fusion network for semantic segmentation of high spatial resolution remote sensing images |
| topic | semantic segmentation high spatial resolution remote sensing images memory efficiency |
| url | https://www.mdpi.com/2072-4292/14/22/5830 |
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