基于语义和边缘特征融合的高分辨率遥感影像水体提取方法

利用卷积神经网络从遥感影像中提取水体时，水体对象边缘像素的特征与内部像素的特征之间往往存在较大差异，导致提取结果中边界模糊、内部像素与边缘像素的提取精度差异较大，影响了整体精度的提高。针对如何从高分辨率遥感影像中进行水体高精度、自动化提取的问题，文章首先以高分辨率遥感图像为基础，利用边缘提取算法生成边缘图像，然后以高分辨率遥感图像和边缘图像作为输入，建立了语义特征和边缘特征融合的高分辨率遥感图像水体提取模型（Semantic Feature and Edge Feature Fusion Network, SEF-Net），用于从高分辨率遥感图像中提取水体对象。实验结果表明，SEF-Net模型在3个数据集中的召回率（91.97%、92.07%、93.97%），精确率（91.12%、98.37%、97.88%），准确率（89.56%、95.07%、94.06%）和F1分数（91.54%、95.12%、95.88%）均优于对比模型，说明SEF-Net模型从高分辨率遥感图像中提取水体时，具有更高的精度和泛化能力。

Water Extraction from Remote Sensing Images: Method Based on Convolutional Neural Networks

Accurate information on the spatial distribution of water is of great significance for monitoring water resources and applications, urban planning, and social and economic development. Remote sensing image segmentation technology based on convolutional neural networks has become an important approach for extracting the spatial distribution of water from remote sensing images. When only convolutional neural networks are used to extract spatial distributions of water from remote sensing images, there are often large differences between the features of edge and internal pixels of water objects, resulting in high noise, fuzzy boundaries, and large differences in the accuracy of extraction of internal and edge pixels. Improving the precision of edge pixel segmentation is the key to improving the precision of the whole extraction result. In this paper, the edge extraction algorithm is used to generate edge images from original images, and remote sensing images and edge images are taken as inputs to establish a water extraction model of high resolution based on semantic feature and edge feature fusion. A semantic and edge feature fusion network, SEF-NET (Semantic Feature and Edge Feature Fusion Network), is used to extract water objects from high-resolution remote sensing images. SEF-NET consists of an encoder, a multi-parallel cavity convolution module, a decoder, and a classifier. The encoder contains a group of semantic feature extraction units and a group of edge feature extraction units, and each group of feature extraction units can extract 4-level features. The multi-parallel cavity convolution module is composed of four extended convolution layers of different cavity sizes in series, which can obtain feature maps at four scales and add them together with the initial input feature maps to obtain multi-scale semantic feature maps. A 4-level decoding unit is set up for the decoder, which splices semantic feature images and edge feature images in series, and then performs feature fusion and upsampling. This strategy can reduce the feature difference between the edge pixel and the inner pixel of the object to obtain high inter-class discrimination and intra-class consistency. SoftMax was used as a classifier to complete pixel classification and generate the final segmentation results. In this paper, the Gaofen Image Dataset, the high-resolution visible light image water object dataset of the 2020 "Xingtucup" High-resolution Remote Sensing Image Interpretation Software Competition, and eight Gaofen-2 images from 2020 were selected for comparative experiments to extract water. SegNet, DeepLabV3, Refinenet and HED-H CNN were the comparison models. The recall rates (91.97%, 92.07%, 93.97%), accuracy rates (91.12%, 98.37%, 97.88%), precision rates (89.56%, 95.07%, 94.06%) and F1 scores (91.54%, 95.12%, 95.88%) were better than those in the comparison models, indicating that the SEF-NET model had greater accuracy and generalization ability in extracting water from high-resolution remote sensing images. Thus, the SEF-NET model served government decision-making and monitoring water pollution better than the comparison models did.