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摘要：
近年来，随着遥感技术的不断发展，遥感图像在地理信息领域的应用越来越广泛。本文基于深度学习方法，探究了遥感图像水边线提取的可行性，并将其应用于水资源监测、灾害预警等领域。在实验中，我们使用U-Net网络对遥感图像进行了水边线提取，取得了较为满意的效果。
关键词：深度学习；遥感图像；水边线；U-Net网络
Introduction
With the continuous development of remote sensing technology, the application of remote sensing images in the field of geographic information is becoming more and more extensive. One of the important applications is water resources monitoring and disaster warning. In recent years, the extraction of water boundary lines in remote sensing images has become an important research topic in the field of geographic information. The traditional methods for extracting water boundary lines in remote sensing images mainly rely on manual marking or threshold segmentation, which is time-consuming and labor-intensive. With the development of deep learning technology, it has become possible to use deep learning methods to extract water boundary lines in remote sensing images.
In this paper, we propose a water boundary line extraction method based on deep learning for remote sensing images. The U-Net network is used to extract the water boundary line in the remote sensing image. By using this method, the water boundary lines in remote sensing images can be extracted automatically and accurately, greatly improving the efficiency of water boundary line extraction.
Methodology
The U-Net network is commonly used for segmentation tasks in the field of deep learning. In this paper, we use the U-Net network to segment the water region in remote sensing images. The U-Net network contains an encoding part and a decoding part. The encoding part is used to extract features from the input image, and the decoding part is used to restore the feature map to the original size.
The U-Net network is composed of a contracting path and an expansive path. The contracting path is used to extract high-level features from the input image, and the expansive path is used to restore the feature map to the original size.
The detailed network architecture is shown in Figure 1.
Figure 1. The architecture of the U-Net network
The input of the U-Net network is a grayscale remote sensing image. The output is a binary image with the boundary of the water region highlighted.
We use the mean squared error (MSE) loss function and Adam optimizer to train the U-Net network. The training process is carried out on a computer with a high-end GPU to accelerate the training process.
Results
In order to evaluate the performance of the proposed method, we carried out experiments on remote sensing images of water regions. The experimental results show that the proposed method can effectively extract the boundary of the water region.
Figure 2 shows the input remote sensing image, the ground truth image, and the segmentation result of the proposed method.
Figure 2. The input remote sensing image, the ground truth image, and the segmentation result of the proposed method
In Figure 2, the red line is the boundary of the water region, and the blue line is the segmentation result of the proposed method. As can be seen from the image, the proposed method can effectively extract the boundary of the water region.
Applications
The extraction of water boundary lines in remote sensing images has broad application prospects in the field of geographic information. The extracted water boundary lines can be used for water resources monitoring, disaster warning, and land use planning.
For example, in water resources monitoring, the extracted water boundary lines can be used to monitor changes in the water area, water depth, and water quality. In disaster warning, the extracted water boundary lines can be used to predict floods and other water-related disasters. In land use planning, the extracted water boundary lines can be used to identify areas suitable for irrigation and aquaculture.
Conclusion
In this paper, we propose a method for extracting water boundary lines in remote sensing images based on deep learning. The U-Net network is used to extract the water boundary line, and the experimental results show that the proposed method can effectively extract the boundary of the water region. The extraction of water boundary lines in remote sensing images has broad application prospects in the field of geographic information, and can be used for water resources monitoring, disaster warning, and land use planning.