Res50-SimAM-ASPP-Unet: A Semantic Segmentation Model for High-Resolution Remote Sensing Images
High-resolution remote sensing images contain intricate details and complex backgrounds, presenting challenges for traditional segmentation methods, which often struggle with accurate classification and contextual understanding. To address these issues, this study introduces the Res50-SimAM-ASPP-Une...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10804789/ |
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| author | Jiajing Cai Jinmei Shi Yu-Beng Leau Shangyu Meng Xiuyan Zheng Jinghe Zhou |
| author_facet | Jiajing Cai Jinmei Shi Yu-Beng Leau Shangyu Meng Xiuyan Zheng Jinghe Zhou |
| author_sort | Jiajing Cai |
| collection | DOAJ |
| description | High-resolution remote sensing images contain intricate details and complex backgrounds, presenting challenges for traditional segmentation methods, which often struggle with accurate classification and contextual understanding. To address these issues, this study introduces the Res50-SimAM-ASPP-Unet model, a semantic segmentation approach for high-resolution remote sensing image processing tasks. The model integrates ResNet50 as the encoding layer of Unet for robust feature extraction, adds the SimAM attention mechanism to selectively enhance relevant details, and incorporates the ASPP module in the decoding layer to capture multi-scale contextual information. The methodology part analyzes the common ResNet model, the attention mechanism module, and the multi-scale feature extraction module, respectively, and then designs experiments to show the necessity and optimal position of adding Res50, SimAM, and ASPP. Comparative experiments on the LandCover.ai dataset demonstrate that the proposed model significantly outperforms common semantic segmentation networks, achieving a MIoU of 81.1%, MPA of 88.2%, Accuracy of 95.1%, Precision of 92.65%, and an F1 score of 90.45%. These results highlight the model’s effectiveness in delivering high accuracy and adaptability across diverse remote sensing environments, establishing it as a valuable tool for applications requiring precise and scalable image segmentation. |
| format | Article |
| id | doaj-art-ddf8fe5e7a2c4c5eaa955b24d73d2592 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ddf8fe5e7a2c4c5eaa955b24d73d25922024-12-21T00:01:03ZengIEEEIEEE Access2169-35362024-01-011219230119231610.1109/ACCESS.2024.351926010804789Res50-SimAM-ASPP-Unet: A Semantic Segmentation Model for High-Resolution Remote Sensing ImagesJiajing Cai0https://orcid.org/0000-0002-4751-3028Jinmei Shi1https://orcid.org/0000-0002-9021-5464Yu-Beng Leau2https://orcid.org/0000-0002-5386-2734Shangyu Meng3https://orcid.org/0009-0001-1836-289XXiuyan Zheng4https://orcid.org/0009-0007-1034-9950Jinghe Zhou5https://orcid.org/0000-0002-6563-1263College of Information Engineering, Hainan Vocational University of Science and Technology, Haikou, ChinaCollege of Information Engineering, Hainan Vocational University of Science and Technology, Haikou, ChinaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, MalaysiaSchool of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaDepartment of Information Engineering, Guangzhou Baiyun Industrial and Commercial Technician College, Guangzhou, ChinaCollege of Information Engineering, Hainan Vocational University of Science and Technology, Haikou, ChinaHigh-resolution remote sensing images contain intricate details and complex backgrounds, presenting challenges for traditional segmentation methods, which often struggle with accurate classification and contextual understanding. To address these issues, this study introduces the Res50-SimAM-ASPP-Unet model, a semantic segmentation approach for high-resolution remote sensing image processing tasks. The model integrates ResNet50 as the encoding layer of Unet for robust feature extraction, adds the SimAM attention mechanism to selectively enhance relevant details, and incorporates the ASPP module in the decoding layer to capture multi-scale contextual information. The methodology part analyzes the common ResNet model, the attention mechanism module, and the multi-scale feature extraction module, respectively, and then designs experiments to show the necessity and optimal position of adding Res50, SimAM, and ASPP. Comparative experiments on the LandCover.ai dataset demonstrate that the proposed model significantly outperforms common semantic segmentation networks, achieving a MIoU of 81.1%, MPA of 88.2%, Accuracy of 95.1%, Precision of 92.65%, and an F1 score of 90.45%. These results highlight the model’s effectiveness in delivering high accuracy and adaptability across diverse remote sensing environments, establishing it as a valuable tool for applications requiring precise and scalable image segmentation.https://ieeexplore.ieee.org/document/10804789/Segmentation of high-resolution remote sensing imagesmulti-scale void space pyramid pool ASPP moduleattention mechanism SimAM moduleRes50-SimAM-ASPP-Unet |
| spellingShingle | Jiajing Cai Jinmei Shi Yu-Beng Leau Shangyu Meng Xiuyan Zheng Jinghe Zhou Res50-SimAM-ASPP-Unet: A Semantic Segmentation Model for High-Resolution Remote Sensing Images IEEE Access Segmentation of high-resolution remote sensing images multi-scale void space pyramid pool ASPP module attention mechanism SimAM module Res50-SimAM-ASPP-Unet |
| title | Res50-SimAM-ASPP-Unet: A Semantic Segmentation Model for High-Resolution Remote Sensing Images |
| title_full | Res50-SimAM-ASPP-Unet: A Semantic Segmentation Model for High-Resolution Remote Sensing Images |
| title_fullStr | Res50-SimAM-ASPP-Unet: A Semantic Segmentation Model for High-Resolution Remote Sensing Images |
| title_full_unstemmed | Res50-SimAM-ASPP-Unet: A Semantic Segmentation Model for High-Resolution Remote Sensing Images |
| title_short | Res50-SimAM-ASPP-Unet: A Semantic Segmentation Model for High-Resolution Remote Sensing Images |
| title_sort | res50 simam aspp unet a semantic segmentation model for high resolution remote sensing images |
| topic | Segmentation of high-resolution remote sensing images multi-scale void space pyramid pool ASPP module attention mechanism SimAM module Res50-SimAM-ASPP-Unet |
| url | https://ieeexplore.ieee.org/document/10804789/ |
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