A High-Precision Water Body Extraction Method Based on Improved Lightweight U-Net

The rapid, accurate extraction of water body information is critical for water resource management and disaster assessment. Its data foundation was mostly provided by remote sensing images through deep learning methods. However, the methods still require the improvement of recognition accuracy and r...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 17; p. 4127
Main Authors An, Shihao, Rui, Xiaoping
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2022
Subjects
Accuracy
Artificial neural networks
Classification
Coders
convolutional neural networks
Datasets
Deep learning
Extreme values
GF-2
Image resolution
Mathematical models
Medical research
Neural networks
Parameters
Recognition
Remote sensing
Resource management
Satellites
Semantics
Structural design
Structural engineering
Water bodies
water body extraction
Water management
Water resources
Water resources management
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Summary:The rapid, accurate extraction of water body information is critical for water resource management and disaster assessment. Its data foundation was mostly provided by remote sensing images through deep learning methods. However, the methods still require the improvement of recognition accuracy and reduction of model size. As a solution, this paper proposed a new high-precision convolutional neural network for water body extraction. This network’s structural design is based on the assumption that the extraction effect of a convolutional neural network is independent from its parameters number, thus the recognition effect could be effectively improved through reasonable adjustment of the network structure according to characteristics of water bodies on high-resolution remote sensing images. It brings two critical improvements. Firstly, the number of downsampling layers was reduced to adapt to the low resolution of remote sensing imagery. Secondly, the bottleneck structure has also been updated to fit the decoder–encoder framework. The improved bottleneck structures were nested to ensure the transmission of water characteristics information in the model. In comparison with the other five commonly used networks, the new network has achieved the best results (average overall accuracy: 98.31%, parameter benefit value: 0.2625), indicating the extremely high practical value of this approach.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14174127