دورية أكاديمية
Research on water extraction from high resolution remote sensing images based on deep learning
| العنوان: | Research on water extraction from high resolution remote sensing images based on deep learning |
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| المؤلفون: | Peng Wu, Junjie Fu, Xiaomei Yi, Guoying Wang, Lufeng Mo, Brian Tapiwanashe Maponde, Hao Liang, Chunling Tao, WenYing Ge, TengTeng Jiang, Zhen Ren |
| المصدر: | Frontiers in Remote Sensing, Vol 4 (2023) |
| بيانات النشر: | Frontiers Media S.A., 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Geophysics. Cosmic physics LCC:Meteorology. Climatology |
| مصطلحات موضوعية: | remote sensing image, semantic segmentation, deep learning, water extraction, DeepLabV3+, Geophysics. Cosmic physics, QC801-809, Meteorology. Climatology, QC851-999 |
| الوصف: | Introduction: Monitoring surface water through the extraction of water bodies from high-resolution remote sensing images is of significant importance. With the advancements in deep learning, deep neural networks have been increasingly applied to high-resolution remote sensing image segmentation. However, conventional convolutional models face challenges in water body extraction, including issues like unclear water boundaries and a high number of training parameters.Methods: In this study, we employed the DeeplabV3+ network for water body extraction in high-resolution remote sensing images. However, the traditional DeeplabV3+ network exhibited limitations in segmentation accuracy for high-resolution remote sensing images and incurred high training costs due to a large number of parameters. To address these issues, we made several improvements to the traditional DeeplabV3+ network: Replaced the backbone network with MobileNetV2. Added a Channel Attention (CA) module to the MobileNetV2 feature extraction network. Introduced an Atrous Spatial Pyramid Pooling (ASPP) module. Implemented Focal loss for balanced loss computation.Results: Our proposed method yielded significant enhancements. It not only improved the segmentation accuracy of water bodies in high-resolution remote sensing images but also effectively reduced the number of network parameters and training time. Experimental results on the Water dataset demonstrated superior performance compared to other networks: Outperformed the U-Net network by 3.06% in terms of mean Intersection over Union (mIoU). Outperformed the MACU-Net network by 1.03%. Outperformed the traditional DeeplabV3+ network by 2.05%. The proposed method surpassed not only the traditional DeeplabV3+ but also U-Net, PSP-Net, and MACU-Net networks.Discussion: These results highlight the effectiveness of our modified DeeplabV3+ network with MobileNetV2 backbone, CA module, ASPP module, and Focal loss for water body extraction in high-resolution remote sensing images. The reduction in training time and parameters makes our approach a promising solution for accurate and efficient water body segmentation in remote sensing applications. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2673-6187 |
| Relation: | https://www.frontiersin.org/articles/10.3389/frsen.2023.1283615/full; https://doaj.org/toc/2673-6187 |
| DOI: | 10.3389/frsen.2023.1283615 |
| URL الوصول: | https://doaj.org/article/3debb7c66bb148e09b71015815eebf82 |
| رقم الأكسشن: | edsdoj.3debb7c66bb148e09b71015815eebf82 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26736187 |
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| DOI: | 10.3389/frsen.2023.1283615 |