期刊
REMOTE SENSING
卷 10, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/rs10020196
关键词
SAR image; despeckling; dilated convolution; skip connection; residual learning
类别
资金
- Fundamental Research Funds for the Central Universities [2042017kf0180]
- Natural Science Foundation of Hubei Province [ZRMS2016000241]
- National Natural Science Foundation of China [61671334]
- National Key Research and Development Program of China [2016YFB0501403]
In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and a residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows a superior performance over the state-of-the-art methods in both quantitative and visual assessments, especially for strong speckle noise.
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