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

Zhang, Peng; Chen, Lifu; Li, Zhenhong; Xing, Jin; Xing, Xuemin; Yuan, Zhihui

Zhang, Peng; Chen, Lifu; Li, Zhenhong; Xing, Jin; Xing, Xuemin; Yuan, Zhihui.

Affiliation

Zhang P; School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China.
Chen L; Laboratory of Radar Remote Sensing Applications, Changsha University of Science & Technology, Changsha 410014, China.
Li Z; School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China. Lifu.Chen@newcastle.ac.uk.
Xing J; Laboratory of Radar Remote Sensing Applications, Changsha University of Science & Technology, Changsha 410014, China. Lifu.Chen@newcastle.ac.uk.
Xing X; School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK. Lifu.Chen@newcastle.ac.uk.
Yuan Z; School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.

Sensors (Basel) ; 19(16)2019 Aug 16.

Article in En | MEDLINE | ID: mdl-31426396

Key words

CONVOLUTION LONG SHORT-TERM MEMORY (ConvLSTM); classification; convolutional neural network (CNN); deep learning; dense convolutional network (DenseNet); global convolutional network (GCN); shadow extraction; synthetic aperture radar (SAR); water extraction

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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sensors (Basel) Year: 2019 Document type: Article Affiliation country:

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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sensors (Basel) Year: 2019 Document type: Article Affiliation country: