FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis
Semantic segmentation of vegetation in aerial remote sensing images is a critical aspect of vegetation mapping. Accurate vegetation segmentation effectively informs real-world production and construction activities. However, the presence of species heterogeneity, seasonal variations, and feature dis...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4194 |
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| author | Bingnan He Dongyang Wu Li Wang Sheng Xu |
| author_facet | Bingnan He Dongyang Wu Li Wang Sheng Xu |
| author_sort | Bingnan He |
| collection | DOAJ |
| description | Semantic segmentation of vegetation in aerial remote sensing images is a critical aspect of vegetation mapping. Accurate vegetation segmentation effectively informs real-world production and construction activities. However, the presence of species heterogeneity, seasonal variations, and feature disparities within remote sensing images poses significant challenges for vision tasks. Traditional machine learning-based methods often struggle to capture deep-level features for the segmentation. This work proposes a novel deep learning network named FA-HRNet that leverages the fusion of attention mechanism and a multi-branch network structure for vegetation detection and segmentation. Quantitative analysis from multiple datasets reveals that our method outperforms existing approaches, with improvements in MIoU and PA by 2.17% and 4.85%, respectively, compared with the baseline network. Our approach exhibits significant advantages over the other methods regarding cross-region and cross-scale capabilities, providing a reliable vegetation coverage ratio for ecological analysis. |
| format | Article |
| id | doaj-art-226251b1d7c8419d8154e70fd7779e54 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-226251b1d7c8419d8154e70fd7779e542025-08-20T01:54:04ZengMDPI AGRemote Sensing2072-42922024-11-011622419410.3390/rs16224194FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and AnalysisBingnan He0Dongyang Wu1Li Wang2Sheng Xu3College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, ChinaSemantic segmentation of vegetation in aerial remote sensing images is a critical aspect of vegetation mapping. Accurate vegetation segmentation effectively informs real-world production and construction activities. However, the presence of species heterogeneity, seasonal variations, and feature disparities within remote sensing images poses significant challenges for vision tasks. Traditional machine learning-based methods often struggle to capture deep-level features for the segmentation. This work proposes a novel deep learning network named FA-HRNet that leverages the fusion of attention mechanism and a multi-branch network structure for vegetation detection and segmentation. Quantitative analysis from multiple datasets reveals that our method outperforms existing approaches, with improvements in MIoU and PA by 2.17% and 4.85%, respectively, compared with the baseline network. Our approach exhibits significant advantages over the other methods regarding cross-region and cross-scale capabilities, providing a reliable vegetation coverage ratio for ecological analysis.https://www.mdpi.com/2072-4292/16/22/4194computer visionaerial remote sensing imagesvision modelssemantic segmentationvegetation coveragepanoptic segmentation |
| spellingShingle | Bingnan He Dongyang Wu Li Wang Sheng Xu FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis Remote Sensing computer vision aerial remote sensing images vision models semantic segmentation vegetation coverage panoptic segmentation |
| title | FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis |
| title_full | FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis |
| title_fullStr | FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis |
| title_full_unstemmed | FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis |
| title_short | FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis |
| title_sort | fa hrnet a new fusion attention approach for vegetation semantic segmentation and analysis |
| topic | computer vision aerial remote sensing images vision models semantic segmentation vegetation coverage panoptic segmentation |
| url | https://www.mdpi.com/2072-4292/16/22/4194 |
| work_keys_str_mv | AT bingnanhe fahrnetanewfusionattentionapproachforvegetationsemanticsegmentationandanalysis AT dongyangwu fahrnetanewfusionattentionapproachforvegetationsemanticsegmentationandanalysis AT liwang fahrnetanewfusionattentionapproachforvegetationsemanticsegmentationandanalysis AT shengxu fahrnetanewfusionattentionapproachforvegetationsemanticsegmentationandanalysis |