College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
College of Information Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu, China.
PLoS One. 2022 Aug 5;17(8):e0272317. doi: 10.1371/journal.pone.0272317. eCollection 2022.
Extracting water bodies from remote sensing images is important in many fields, such as in water resources information acquisition and analysis. Conventional methods of water body extraction enhance the differences between water bodies and other interfering water bodies to improve the accuracy of water body boundary extraction. Multiple methods must be used alternately to extract water body boundaries more accurately. Water body extraction methods combined with neural networks struggle to improve the extraction accuracy of fine water bodies while ensuring an overall extraction effect. In this study, false color processing and a generative adversarial network (GAN) were added to reconstruct remote sensing images and enhance the features of tiny water bodies. In addition, a multi-scale input strategy was designed to reduce the training cost. We input the processed data into a new water body extraction method based on strip pooling for remote sensing images, which is an improvement of DeepLabv3+. Strip pooling was introduced in the DeepLabv3+ network to better extract water bodies with a discrete distribution at long distances using different strip kernels. The experiments and tests show that the proposed method can improve the accuracy of water body extraction and is effective in fine water body extraction. Compared with seven other traditional remote sensing water body extraction methods and deep learning semantic segmentation methods, the prediction accuracy of the proposed method reaches 94.72%. In summary, the proposed method performs water body extraction better than existing methods.
从遥感图像中提取水体在许多领域都很重要，例如水资源信息的获取和分析。传统的水体提取方法增强了水体与其他干扰水体之间的差异，以提高水体边界提取的准确性。需要交替使用多种方法来更准确地提取水体边界。结合神经网络的水体提取方法在确保整体提取效果的同时，努力提高精细水体的提取精度。在这项研究中，我们添加了假彩色处理和生成对抗网络（GAN）来重建遥感图像并增强微小水体的特征。此外，我们还设计了一种多尺度输入策略来降低训练成本。我们将处理后的数据输入到一种新的基于带状池化的遥感图像水体提取方法中，这是 DeepLabv3+的改进。带状池化被引入到 DeepLabv3+网络中，以使用不同的带状核更好地提取远距离离散分布的水体。实验和测试表明，所提出的方法可以提高水体提取的准确性，并且在精细水体提取方面非常有效。与其他七种传统的遥感水体提取方法和深度学习语义分割方法相比，所提出方法的预测精度达到了 94.72%。总之，所提出的方法在水体提取方面优于现有的方法。
