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

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

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 Convol...

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Bibliographic Details
Main Authors: Peng Zhang, Lifu Chen, Zhenhong Li, Jin Xing, Xuemin Xing, Zhihui Yuan
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Sensors
Subjects:
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)
Online Access:https://www.mdpi.com/1424-8220/19/16/3576

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