Abstract. In this paper, we proposed an azimuth-range decouple-based L1 regularization method for wide ScanSAR imaging via extended chirp scaling (ECS) and applied it to the TerraSAR-X data to achieve large-scale sparse reconstruction. Compared with ECS, the conventional ScanSAR imaging algorithm based on matched filtering, the proposed method can improve the synthetic aperture radar image performance with full-sampling raw data for not only sparse but also nonsparse surveillance regions. It can also achieve high-resolution imaging for sparse considered scenes efficiently using down-sampling raw data. Compared with a typical L1 regularization imaging approach, which requires transfer of the two-dimensional (2-D) echo data into a vector and reconstruction of the scene via 2-D matrix operation, our proposed method has less computational cost and hence makes the large-scale regularization reconstruction of considered area become possible. The experimental results via real data validate the effectiveness of the proposed method.     «

Abstract. In this paper, we proposed an azimuth-range decouple-based L1 regularization method for wide ScanSAR imaging via extended chirp scaling (ECS) and applied it to the TerraSAR-X data to achieve large-scale sparse reconstruction. Compared with ECS, the conventional ScanSAR imaging algorithm based on matched filtering, the proposed method can improve the synthetic aperture radar image performance with full-sampling raw data for not only sparse but also nonsparse surveillance regions. It can...     »