Title :
On the Performance of Reweighted
Minimization for Tomographic SAR Imaging
Author :
Peifeng Ma ; Hui Lin ; Hengxing Lan ; Fulong Chen
Author_Institution :
Inst. of Space & Earth Inf. Sci., Chinese Univ. of Hong Kong, Hong Kong, China
Abstract :
L1 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 L1 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.
Keywords :
digital elevation models; electromagnetic wave scattering; geophysical image processing; image resolution; iterative methods; minimisation; optical radar; radar imaging; remote sensing by radar; synthetic aperture radar; tomography; LiDAR; TanDEM-X images; TerraSAR-X images; height estimation; iterative reweighted L1 minimization; light detection and ranging; model selection; penalization rule; persistent scatterer extraction; signal-to-noise ratio; sparse inversion; super-resolution capability; synthetic aperture radar; tomographic SAR imaging; weight factor; Image reconstruction; Minimization; Remote sensing; Signal to noise ratio; Synthetic aperture radar; Tomography; Reweighted $L_{1}$ minimization; TerraSAR-X/TanDEM-X; tomographic synthetic aperture radar (SAR) imaging; urban areas;
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2014.2365613