基于混合稀疏表示的二维压缩感知SAR成像

2-D compressed sensing SAR imaging based on mixed sparse representation

压缩感知(CS)理论在合成孔径雷达(SAR)成像中应用广泛。针对包含城市、河流等区域的非稀疏场景压缩感知SAR成像,提出基于近似观测模型的混合稀疏表示(MSR)压缩感知SAR成像方法。该方法将复杂的SAR图像分解成点、线、面,并将线、面分别通过离散余弦变换和曲波变换转换到稀疏域,使压缩感知的稀疏性条件得以满足,通过求解基于近似观测模型的二维压缩感知优化问题重建非稀疏场景的SAR图像。所提方法能够实现降采样率条件下对包含城市、河流等非稀疏场景区域的成像,仿真场景和实测场景成像结果表明了所提方法的有效性。

Compressed sensing(CS) theory has been applied in synthetic aperture radar(SAR) in the recent years.A 2-D CS SAR imaging method is proposed using mixed sparse representation(MSR) based on approximate observation model in non-sparse scene compressed sensing SAR imaging.Firstly,non-sparse scene with complicated ground features is decomposed into point-like,edges and smooth components.Then,edges and smooth components are transformed into sparse domain by discrete cosine transform and curvelet transform respectively.And based on approximate observation model,SAR images are derived from 2-D CS optimization problem.Owing to the sparse representation method of non-sparse scene,the proposed method can realize high quality SAR imaging for non-sparse scene.Compared to the existing method,the proposed method has better reconstruction quality for region containing distinct edges and lines,such as city and river.Both the simulation scene and real scene experiments demonstrate the effectiveness of the proposed method.