Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks

Guojie Wang,Huihui Song,Mengjuan Wu,Xikun Wei

doi:10.3390/rs12050795

Abstract

The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment. However, there are significant limitations in the traditionally used index for water body identification. In this study, we have proposed a deep convolutional neural network (CNN), based on the multidimensional densely connected convolutional neural network (DenseNet), for identifying water in the Poyang Lake area. The results from DenseNet were compared with the classical convolutional neural networks (CNNs): ResNet, VGG, SegNet and DeepLab v3+, and also compared with the Normalized Difference Water Index (NDWI). Results have indicated that CNNs are superior to the water index method. Among the five CNNs, the proposed DenseNet requires the shortest training time for model convergence, besides DeepLab v3+. The identification accuracies are evaluated through several error metrics. It is shown that the DenseNet performs much better than the other CNNs and the NDWI method considering the precision of identification results; among those, the NDWI performance is by far the poorest. It is suggested that the DenseNet is much better in distinguishing water from clouds and mountain shadows than other CNNs.

Highlights

Water is an indispensable resource for a sustainable ecosystem on earth
This study proposed an Alternating Direction of Method of Multipliers (ADMM) approach to separate the foreground information from the background, and it has a great effect upon the separation of text, moving objects and so on
We compare with the results derived from the Normalized Difference Water Index (NDWI) index and four other deep neural networks of VGG, ResNet, SegNet and DeepLab v3+

Summary

Introduction

It contributes significantly to the balance of ecosystems, the maintenance of climate change and the carbon cycle [1]. The formation, expansion, shrinkage and disappearance of surface water are important factors influencing the environment and regional climate changes. A lot of research has been done on image foreground extraction and segmentation [13]. This study proposed an Alternating Direction of Method of Multipliers (ADMM) approach to separate the foreground information from the background, and it has a great effect upon the separation of text, moving objects and so on. There are many algorithms for extracting water from remote sensing images, including spectral classification [14], the threshold segmentation method [7,15] and machine learning [16,17,18]

Methods

Results

Discussion

Conclusion