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

Abstract

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.

Keywords

• convolutional neural network
• high resolution remote sensing image
• semantic features
• edge features
• water body extraction