A CNN-BASED METHOD FOR SAR IMAGE DESPECKLING

被引:0
|
作者
Ma, Dejiao [1 ]
Zhang, Xiaoling [1 ]
Tang, Xinxin [1 ]
Ming, Jing [1 ]
Shi, Jun [1 ]
机构
[1] Univ Elect Sci & Technol China, Chengdu 611731, Sichuan, Peoples R China
关键词
SAR image despeckling; convolutional neural networks; dilated convolution; residual learning;
D O I
10.1109/igarss.2019.8899122
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
In this paper, to remove the speckle noise of SAR images, we propose a modified method for SAR image despeckling based on Convolutional Neural Networks (CNNs). The network uses dilated convolutions for feature extraction, which can extend the receptive field and prevent too many layers that may result in computational burden and low efficiency. The network also uses residual learning to accelerate training procedure and improve performance for SAR image despeckling. Experimental results show that the proposed method achieve good performance for SAR image despeckling both on simulated and real data. And compared with the traditional despeckling methods, the proposed method has better performance and higher efficiency.
引用
收藏
页码:4272 / 4275
页数:4
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