Abstract
Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.
Highlights
Water is the basic substance for human society’s production and development [1]
A new convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) for water body extraction for GF-1 high-resolution satellite images is proposed in this study
Three convolutional neural networks (CNNs) that conduct semantic segmentation in computer vision field are employed for comparison
Summary
Water is the basic substance for human society’s production and development [1]. Surface water bodies play important roles in Earth’s material and energy cycles [2,3]. With the development of machine learning, traditional machine learning algorithms, such as decision tree (DT) [15], support vector machine (SVM) [6], and random forest (RF) [9], have been widely used in water body extraction. These algorithms perform classification by using artificially designed features, including spectral and textural features. Different feature vectors are needed for different images and the feature vectors have great impacts on the final classification results These issues make applying machine learning to water extraction challenging
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