Water Body Automated Extraction in Polarization SAR Images With Dense-Coordinate-Feature-Concatenate Network

Synthetic aperture radar (SAR) water body extraction is of great significance for many applications, such as flood disaster monitoring, coastline change detection, and water resources management. However, the previous research works have mainly focused on single-polarization SAR images, and the dual...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.12073-12087
Main Authors: Xue, Weibao, Yang, Hui, Wu, Yanlan, Kong, Peng, Xu, Hao, Wu, Penghai, Ma, Xiaoshuang
Format: Article
Language:English
Subjects:
Accuracy
Deep learning
Deep learning methods
dual-polarimetric
Feature extraction
Flood management
Modules
Polarimetry
Polarization
Radar polarimetry
Redundancy
SAR (radar)
Shades
Synthetic aperture radar
synthetic aperture radar (SAR)
Training data
Water
Water bodies
water body extraction
Water management
Water resources
Water resources management
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Synthetic aperture radar (SAR) water body extraction is of great significance for many applications, such as flood disaster monitoring, coastline change detection, and water resources management. However, the previous research works have mainly focused on single-polarization SAR images, and the dual-polarimetric information has not been comprehensively utilized, which result in the problem of incomplete water body extraction. In this article, we propose a model called dense-coordinate-feature-concatenate network to extract and fuse the water features of dual-polarimetric SAR images, overcome the influence of ground interferences, and improve the accuracy and generalization of the model. In this model, the improved dense block module is used to extract and transfer the water features in the dual-polarimetric data, a coordinate attention module is used to reduce the loss of water body boundary, and a feature concatenate module is proposed to avoid information redundancy. Experimental results show that our proposed model can not only overcome the influence of roads and hill shades and achieve 98.4%, 84.62%, and 91.67% water body extraction accuracy in overall accuracy, intersection over union, and F1, respectively, on Gaofen-3 SAR images but also be directly applied to the water body extraction of Sentinel-1 SAR images.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2021.3129182