Self-Calibrated Convolutional Neural Network for SAR Image Despeckling.

Yuan, Ye and Jiang, Yan and Wu, Yanxia and Jiang, Richard (2021) Self-Calibrated Convolutional Neural Network for SAR Image Despeckling. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS :. IEEE, pp. 399-402. ISBN 9781665447621

Full text not available from this repository.


Synthetic aperture radar (SAR) images are contaminated by speckle noise, which has largely limited its practical applications. Recently, convolutional neural networks (CNNs) have indicated good potential for various image processing tasks. In this paper, we propose a self-calibrated convolutional neural network for SAR image despeckling, called SAR-SCCNN. To enlarge the receptive field of the network, downsampling and dilated convolutions are employed in each self-calibrated block. Also, the contextual information from spaces with different scales is extracted and concentrated to obtain accurate despeckled images. Experiments on synthetic speckled and real SAR data are conducted to perform the subjective visual assessment of image quality and objective evaluation. Results show that our proposed method can effectively suppress speckle noise and preserve detailed features.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
Deposited By:
Deposited On:
26 Oct 2022 10:15
Last Modified:
16 Jul 2024 05:10