On the Performance of Reweighted L-1 Minimization for Tomographic SAR Imaging
文献类型：SCI/SSCI论文
| 作者 | Ma P. F.; Lin, H.; Lan, H. X.; Chen, F. L. |
| 发表日期 | 2015 |
| 关键词 | Reweighted L-1 minimization TerraSAR-X/TanDEM-X tomographic synthetic aperture radar (SAR) imaging urban areas regularization scatterers |
| 英文摘要 | L-1 minimization has proven to be useful for tomographic synthetic aperture radar (SAR) imaging because it has super-resolution capability and produces no sidelobes. However, it cannot always derive the sparsest solution and often yields outliers in recovery. Consequently, it is usually difficult to extract true persistent scatterers straightforwardly in practice. To enhance the sparsity, we introduce iterative reweighted L-1 minimization for sparse inversion. The weight factor is computed in each iteration, according to the previous tomographic magnitude to establish a more democratic penalization rule. Our simulation results indicate that the reweighted algorithm can achieve perfect recovery when noise is lower. Specifically, when the signal-to-noise ratio is equal to 5 dB, two reweighted iterations can improve the probability of true sparsity from 29.2% to 99.8% for single scatterers and from 0.2% to 95.4% for double scatterers. Due to the enhanced sparsity, we can directly identify scatterers without the need for further model selection. The method is validated using 44 TerraSAR-X/TanDEM-X images. Single and double scatterers are detected in urban areas. Verification using light detection and ranging (LiDAR) data indicates that we achieve submeter accuracy of the height estimates. |
| 出处 | Ieee Geoscience and Remote Sensing Letters |
| 卷 | 12 |
| 期 | 4 |
| 页 | 895-899 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 1545-598X |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/38471]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Ma P. F.,Lin, H.,Lan, H. X.,et al. On the Performance of Reweighted L-1 Minimization for Tomographic SAR Imaging. 2015. |
入库方式： OAI收割
来源：地理科学与资源研究所
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