中山大学学报（自然科学版）（中英文）2024，Vol.63Issue(4)：107-114,8.DOI:10.13471/j.cnki.acta.snus.ZR20230037
SAR图像稀疏表示模型的实证研究
Empirical study on sparse representation model of SAR images
摘要
Abstract
An example of using the sparse representation algorithm to obtain a basis function dictionary for synthetic-aperture radar(SAR)scenes.The comparative of sparse representation of images before and after filtering shows that speckle noise has an impact on the dictionary results of sparse representation of SAR scenes.Select specific SAR image data from Pujiang No.2,ALOS2,and SIR-C,it is discussed that the effects of optimization algorithm,sample content,dataset size,radar resolution,polarization method,and band on dictionary results by setting single factor conditions.The results show that:(1)The dictionary learned from sparse representation of SAR scenes is related to radar band,resolution and polarization mode,and is independent of sample contents,datasets size,and optimization algorithms.(2)C-band can better reflect the sparsity of SAR scenes than L band.(3)The downsampling dataset can better reflect the sparsity of SAR scenes.(4)The dictionaries learned from HH and VV polarized images have more essential features.关键词
SAR/稀疏表示/基函数字典/相干斑噪声/极化Key words
SAR/sparse representation/base function dictionary/speckle noise/polarization分类
信息技术与安全科学引用本文复制引用
黄柯蒙,姜娜娜,赵文博,郑妍昕,刘文平,朱炬波..SAR图像稀疏表示模型的实证研究[J].中山大学学报（自然科学版）（中英文）,2024,63(4):107-114,8.基金项目
国家自然科学基金(U21B2039) （U21B2039）
