Research on water extraction from high resolution remote sensing images based on deep learning

作者：Peng Wu, Junjie Fu, Xiaomei Yi, Guoying Wang, Lufeng Mo, Brian Tapiwanashe Maponde, Hao Liang, Chunling Tao, Wenying Ge, Tengteng Jiang, Zhen Ren · 发表于：Frontiers in Remote Sensing · 年份：2023 · DOI：10.3389/frsen.2023.1283615 · 被引用次数：21 · 研究领域：Flood Risk Assessment and Management、Remote Sensing and LiDAR Applications、Automated Road and Building Extraction

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: Outperf...