SAR Tomography Based on Block Compressive Sensing

Abstract

While the use of SAR Tomography (TomoSAR) based on Compressive Sensing (CS) makes it possible to reconstruct the height profile of an observed scene, the performance of the reconstruction decreases for a structural observed scene. To deal with this issue, we propose using TomoSAR based on Block Compressive Sensing (BCS), which changes the reconstruction of the structural observed scene into a BCS problem under the principles of CS. Further, the block size is established by utilizing the relationship between the characteristics of the structural observed scene and the SAR parameters, such that the BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model. Compared with existing CSTomoSAR methods, the proposed BCS-TomoSAR method makes better use of the sparsity and structure information of a structural observed scene, and has higher precision and better reconstruction performance. We used simulations and Radarsat-2 data to verify the effectiveness of this proposed method.

Keywords

• SAR Tomography (TomoSAR)
• Compressive Sensing (CS)
• Block Compressive Sensing (BCS)
• Sparsity
• Structure