CAJ | 학술논문

提出一种稀疏描述与结构特征相结合的极化合成孔径雷达（polarimetric synthetic aperture radar， PolSAR）图像斑点抑制算法。首先利用图像的极化信息对原图像按结构特征分类，形成分类标记图；然后采用正交匹配追踪（orthogonal matching pursuit，OMP）算法对图像进行稀疏分解，利用 K 奇异值分解（K-singular value decomposition，K-SVD）算法对过完备字典进行训练更新，得到图像相应的训练字典和稀疏系数，重构图像；最后在重构图像中按分类图增强相应的点线目标。利用美国 AIRSAR 系统采集的半月湾地区数据进行实验表明：该方法在实现图像去噪的同时，能够有效的保持地物的散射特性。
제출일충희소묘술여결구특정상결합적겁화합성공경뢰체（polarimetric synthetic aperture radar， PolSAR）도상반점억제산법。수선이용도상적겁화신식대원도상안결구특정분류，형성분류표기도；연후채용정교필배추종（orthogonal matching pursuit，OMP）산법대도상진행희소분해，이용 K 기이치분해（K-singular value decomposition，K-SVD）산법대과완비자전진행훈련경신，득도도상상응적훈련자전화희소계수，중구도상；최후재중구도상중안분류도증강상응적점선목표。이용미국 AIRSAR 계통채집적반월만지구수거진행실험표명：해방법재실현도상거조적동시，능구유효적보지지물적산사특성。
This paper presents a novel speckle reduction algorithm based on sparse representation and struc-ture characters of the polarimetric synthetic aperture radar (PolSAR)image.Firstly,the image is classified ac-cording to the polarimetric properties to form the classification map.Secondly,the orthogonal matching pursuit (OMP)algorithm is applied to implement the sparse decomposition on PolSAR images,and the over-complete dictionary is updated by the K-singular value decomposition (K-SVD)algorithm to get the trained dictionary and sparse coefficients,with which the image without noise is reconstructed.Finally,the point and line targets are enhanced by the classification map in the reconstructed image.Experimental results with the data of the Hay-ward area from the AIRSAR system show that the proposed method is effective both on speckle reduction and scattering characteristics preservation.