基于聚类字典学习和稀疏表示的SAR图像抑斑方法

SAR despeckling based on clustering dictionary learning and sparse representation

针对合成孔径雷达(synthetic aperture radar,SAR)图像相干斑抑制问题,提出一种基于聚类字典学习和稀疏表示的SAR图像抑斑方法。本方法以相干斑噪声的非对数加性模型为基础,通过改进相似度测度的K-means聚类和主成分分析方法进行字典学习,克服了相干斑噪声非高斯性带来的影响,形成具有结构性聚类的字典原子;在稀疏分解方面,通过引入方差稳定因子,建立了适用于抑制SAR相干斑噪声的稀疏表示模型,并通过交替迭代算法进行代价方程求解;同时算法还增加了点目标保护措施,避免了对图像点目标"过滤波"。通过卫星、无人机SAR图像的抑斑实验证明,相比经典的SAR图像抑斑方法,所提的方法在抑斑的视觉效果上和客观评价指标上都有较大的提升。

Aiming at the speckle reduction of aperture radar synthetic （SAR ） images, a method of SAR despeckling based on clustering dictionary learning and sparse representation is proposed. Based on the non-log-arithmic model of the coherent speckle noise , the K - means clustering with the improved similarity measure and principal component analysis method, the dictionary atoms with structural clustering are obtained, which over-comes the effect of the non-Gaussian of the speckle noise. A sparse representation model combining clustering and sparsity under a unified framework is established. An iterative algorithm is proposed for solving the cost e-quation. Meanwhile,the point target protection measure is introduced into the algorithm to avoid the ＂over fil -tering＂ of the point target. Experimental results with SAR images from satellites and unmanned aerial vehicle show that compared with the existing SAR despeckling methods, the proposed method has a great improvement in both the visual effect and the objective evaluation indexes.