Speckle Reduction of SAR Image through Dictionary Learning and Point Target Enhancing Approaches

摘要：

Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. In this paper, we proposed a speckle reduction technique based on sparse representation and dictionary learning. Firstly, an adaptive dictionary was learned by performing KSVD algorithm through a large amount of training patches extracted from the noisy SAR image. Considering the inaccurate recovery of point targets which is brought by the inadequate number of training samples, we employed a point target enhancing scheme to highlight the important point targets in the SAR image. Some experiments were conducted on real SAR images, and the results shows that our proposed algorithm can effectively reduce the speckle noise as well as preserve details. Some comparisons are made to prove its superiority to the available algorithms.

关键词： Speckle Reduction dictionary learning sparse representation point target enhancing

作者: Shuyuan Yang Yueyuan Zhang Yue Han

作者单位: Key Lab of Intelligent Perception and Image Understanding of Ministry of Education,Department of Electrical Engineering, Xidian University, Xi’an, China, 710071

会议类型: 国际会议

会议名称: 2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

会议地点: 成都

会议语种:英文

页码: 1926-1929

在线出版日期: 2011-10-24（万方平台首次上网日期，不代表论文的发表时间）