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Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network

1,2, 1,2,3,*, 3,4, 3, 2,5 and 1,2
1
School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
2
Laboratory of Radar Remote Sensing Applications, Changsha University of Science & Technology, Changsha 410014, China
3
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
4
College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
5
School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3576; https://doi.org/10.3390/s19163576 (registering DOI)
Received: 7 July 2019 / Revised: 10 August 2019 / Accepted: 12 August 2019 / Published: 16 August 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Abstract

The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (MRDED) network is proposed, which integrates Convolutional Neural Network (CNN), Residual Network (ResNet), Dense Convolutional Network (DenseNet), Global Convolutional Network (GCN), and Convolutional Long Short-Term Memory (ConvLSTM). MRDED contains three parts: the Gray Level Gradient Co-occurrence Matrix (GLGCM), the Encoder network, and the Decoder network. GLGCM is used to extract low-level features, which are further processed by the Encoder. The Encoder network employs ResNet to extract features at different resolutions. There are two components of the Decoder network, namely, the Multi-level Features Extraction and Fusion (MFEF) and Score maps Fusion (SF). We implement two versions of MFEF, named MFEF1 and MFEF2, which generate separate score maps. The difference between them lies in that the Chained Residual Pooling (CRP) module is utilized in MFEF2, while ConvLSTM is adopted in MFEF1 to form the Improved Chained Residual Pooling (ICRP) module as the replacement. The two separate score maps generated by MFEF1 and MFEF2 are fused with different weights to produce the fused score map, which is further handled by the Softmax function to generate the final extraction results for water and shadow areas. To evaluate the proposed framework, MRDED is trained and tested with large SAR images. To further assess the classification performance, a total of eight different classification frameworks are compared with our proposed framework. MRDED outperformed by reaching 80.12% in Pixel Accuracy (PA) and 73.88% in Intersection of Union (IoU) for water, 88% in PA and 77.11% in IoU for shadow, and 95.16% in PA and 90.49% in IoU for background classification, respectively. View Full-Text
Keywords: water extraction; shadow extraction; deep learning; synthetic aperture radar (SAR); classification; convolutional neural network (CNN); global convolutional network (GCN); dense convolutional network (DenseNet); CONVOLUTION LONG SHORT-TERM MEMORY (ConvLSTM) water extraction; shadow extraction; deep learning; synthetic aperture radar (SAR); classification; convolutional neural network (CNN); global convolutional network (GCN); dense convolutional network (DenseNet); CONVOLUTION LONG SHORT-TERM MEMORY (ConvLSTM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Zhang, P.; Chen, L.; Li, Z.; Xing, J.; Xing, X.; Yuan, Z. Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network. Sensors 2019, 19, 3576.

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