Research on water extraction from high resolution remote sensing images based on deep learning - Wu, Peng; Fu, Junjie; Yi, Xiaomei; Wang, Guoying; Mo, Lufeng; Maponde, Brian Tapiwanashe; Liang, Hao; Tao, Chunling; Ge, WenYing; Jiang, TengTeng; Ren, Zhen (Frontiers in Remote Sensing, 2023)
The extraction of water bodies from high-resolution remote sensing images plays a crucial role in monitoring surface water. In recent years, with the development of deep learning, some deep neural networks have been applied in the field of high-resolution remote sensing image segmentation. However, general convolutional models face several issues in water body extraction, such as blurry water boundaries and a large number of training parameters. In this paper, we employ the DeeplabV3+ network for water body extraction in high-resolution remote sensing images. However, the traditional DeeplabV3+ network exhibits poor segmentation accuracy for high-resolution remote sensing images and has a large number of parameters, resulting in high training costs. Therefore, the traditional DeeplabV3+ network was improved by replacing the backbone network with MobilenetV2, adding CA module to the MobileNetV2 feature extraction network and ASPP module, and introducing Focal loss balancing loss.The proposed method not only enhances the segmentation accuracy of water bodies in high-resolution remote sensing images but also effectively reduces the number of network parameters and training time. Experimental results on the Water dataset demonstrate that the proposed method outperforms the U-Net network by 3.06% in terms of mIoU (mean Intersection over Union), outperforms the MACU-Net network by 1.03%, and outperforms the traditional DeeplabV3+ network by 2.05%. It surpasses the traditional DeeplabV3+, U-Net, PSP-Net, and MACU-Net networks.