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SAR Image Despeckling Based on Block-Matching and Noise-Referenced Deep Learning Method
| Content Provider | MDPI |
|---|---|
| Author | Wang, Chen Yin, Zhixiang Ma, Xiaoshuang Yang, Zhutao |
| Copyright Year | 2022 |
| Description | The noise2noise-based despeckling method, capable of training the despeckling deep neural network with only noisy synthetic aperture radar (SAR) image, has presented very good performance in recent research. This method requires a fine-registered multi-temporal dataset with minor time variance and uses similarity estimation to compensate for the time variance. However, constructing such a training dataset is very time-consuming and may not be viable for a certain practitioner. In this article, we propose a novel single-image-capable speckling method that combines the similarity-based block-matching and noise referenced deep learning network. The denoising network designed for this method is an encoder–decoder convolutional neural network and is accommodated to small image patches. This method firstly constructs a large number of noisy pairs as training input by similarity-based block-matching in either one noisy SAR image or multiple images. Then, the method trains the network in a Siamese manner with two parameter-sharing branches. The proposed method demonstrates favorable despeckling performance with both simulated and real SAR data with respect to other state-of-the-art reference filters. It also presents satisfying generalization capability as the trained network can despeckle well the unseen image of the same sensor. The main advantage of the proposed method is its application flexibility. It could be trained with either one noisy image or multiple images. Furthermore, the despeckling could be inferred by either the ad hoc trained network or a pre-trained one of the same sensor. |
| Starting Page | 931 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14040931 |
| Journal | Remote Sensing |
| Issue Number | 4 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-14 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Block Matching Convolutional Neural Network (cnn) Deep Learning Speckle Filtering Synthetic Aperture Radar (sar) Image Similarity |
| Content Type | Text |
| Resource Type | Article |
