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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Vydáno v:	IEEE geoscience and remote sensing letters Ročník 16; číslo 8; s. 1240 - 1244
Hlavní autoři:	Gao, Feng, Wang, Xiao, Gao, Yunhao, Dong, Junyu, Wang, Shengke
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Dong, Junyu Gao, Yunhao Gao, Feng Wang, Shengke Wang, Xiao
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Snippet	Sea ice change detection from synthetic aperture radar (SAR) images can be regarded as a classification procedure, in which pixels are classified into changed...
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SubjectTerms	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
Title	Sea Ice Change Detection in SAR Images Based on Convolutional-Wavelet Neural Networks
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