Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 59, Issue 5, Pages 3956-3966Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.3014130
Keywords
Consistent cycle spinning (CCS); convolutional neural network (CNN); nonsubsample shearlet transform (NSST); synthetic aperture radar (SAR) speckle removal
Categories
Funding
- Natural Science Foundation of China [61701310, 61401308, 61572063]
- Natural Science Foundation of Hebei Province [F2020201025, F2016201187, F2018210148]
- Science Research Project of Hebei Province [QN2020030, QN2016085]
- Natural Science Foundation of Hebei University [2014-303]
- Opening Foundation of Machine Vision Engineering of Hebei Province [2018HBMV02]
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This article introduces a hybrid denoising approach using CNN and CCS in the NSST domain to remove speckle noise in SAR images and retain more detailed information. The experimental results show that the method achieves better speckle removal performance compared to state-of-the-art algorithms.
Synthetic aperture radar (SAR) images often interfere with speckle artifacts that have a great impact on subsequent processing and analysis operations. To remove speckle artifacts, this article introduces a hybrid denoising approach by using a convolutional neural network (CNN) and consistent cycle spinning (CCS) in the nonsubsample shearlet transform (NSST) domain. First, we apply NSST to a noisy SAR image to gain low- and high-frequency coefficients. Second, we adopt a learned deep CNN model to eliminate the speckle noise in the low-frequency coefficients, which retains more contour information. Third, we employ CCS to enhance the high-frequency coefficients, which preserves more details of the original SAR image. Finally, we obtain the denoised image by using inverse NSST applied to the denoised coefficients. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only achieves better speckle removal performance but also maintains more detailed information retention.
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