Sea Ice Change Detection in SAR Images Based on Convolutional-Wavelet Neural Networks

Sea ice change detection from synthetic aperture radar (SAR) images can be regarded as a classification procedure, in which pixels are classified into changed and unchanged classes. However, existing methods usually suffer from the intrinsic speckle noise of multitemporal SAR images. To solve the pr...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 16; no. 8; pp. 1240 - 1244
Main Authors: Gao, Feng, Wang, Xiao, Gao, Yunhao, Dong, Junyu, Wang, Shengke
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Change detection
Change detection algorithms
Classification
convolutional-wavelet neural network (CWNN)
Detection
Image classification
Image detection
Neural networks
Pixels
Radar detection
Radar imaging
SAR (radar)
Sea ice
Speckle
Synthetic aperture radar
synthetic aperture radar (SAR) images
Training
Wavelet transforms
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary:Sea ice change detection from synthetic aperture radar (SAR) images can be regarded as a classification procedure, in which pixels are classified into changed and unchanged classes. However, existing methods usually suffer from the intrinsic speckle noise of multitemporal SAR images. To solve the problem, this letter presents a change detection method based on convolutional-wavelet neural networks (CWNNs). In CWNN, dual-tree complex wavelet transform is introduced into convolutional neural networks for changed and unchanged pixels' classification, and then, the effect of speckle noise is effectively reduced. In addition, a virtual sample generation scheme is employed to create samples for CWNN training, and the problem of limited samples is alleviated. Experimental results on two real SAR image data sets demonstrate the effectiveness and robustness of the proposed method.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2895656