TY - JOUR
T1 - The comparative study of three nonparametric methods of SAR tomography for building reconstruction
AU - Omati, Mehrnoosh
AU - Sahebi, Mahmod Reza
AU - Aghababaei, H.
N1 - Funding Information:
The authors would like to thank the Iran National Science Foundation (INSF) for generous financial support. The authors also thank the European Space Agency (ESA) for providing the TerraSAR-X data images.
Publisher Copyright:
© 2021 COSPAR
PY - 2022/2/15
Y1 - 2022/2/15
N2 - Over the past years, synthetic aperture radar (SAR) tomography (TomoSAR) has indicated significant potentials for three-dimensional (3D) reconstruction of buildings in urban areas. A large number of SAR images are thus required to perform tomographic inversions in non-parametric spectral estimators. To this end, in the case of a limited choice of SAR acquisitions, the present study aims to evaluate the capabilities of five non-parametric spectral estimation (SE) techniques, including linear prediction (LP), maximum entropy (ME), and minimum norm (MN), Capon, and beamforming (BF) in the tomographic reconstruction of urban environments. The performance analysis is carried out by using both simulated and real SAR datasets. The study results demonstrate that the proposed efficient LP estimator, minimizing the average output signal power over the antenna array elements, can separate the layover scatterers along the height direction. This low-computational SE method is thus able to clean side lobes while using a small number of observations. The proposed algorithms, as applied on TerraSAR-X strip-map images of the city of Tehran, Iran, verify the effectiveness of the non-parametric LP reconstruction technique for urban buildings. The estimated height of the scatterers also indicates that the LP estimator is similar to the field-based measurements once compared with ME, MN, Capon, and BF reconstruction methods.
AB - Over the past years, synthetic aperture radar (SAR) tomography (TomoSAR) has indicated significant potentials for three-dimensional (3D) reconstruction of buildings in urban areas. A large number of SAR images are thus required to perform tomographic inversions in non-parametric spectral estimators. To this end, in the case of a limited choice of SAR acquisitions, the present study aims to evaluate the capabilities of five non-parametric spectral estimation (SE) techniques, including linear prediction (LP), maximum entropy (ME), and minimum norm (MN), Capon, and beamforming (BF) in the tomographic reconstruction of urban environments. The performance analysis is carried out by using both simulated and real SAR datasets. The study results demonstrate that the proposed efficient LP estimator, minimizing the average output signal power over the antenna array elements, can separate the layover scatterers along the height direction. This low-computational SE method is thus able to clean side lobes while using a small number of observations. The proposed algorithms, as applied on TerraSAR-X strip-map images of the city of Tehran, Iran, verify the effectiveness of the non-parametric LP reconstruction technique for urban buildings. The estimated height of the scatterers also indicates that the LP estimator is similar to the field-based measurements once compared with ME, MN, Capon, and BF reconstruction methods.
KW - LINEAR prediction (LP)
KW - Maximum entropy (ME)
KW - Minimum norm (MN)
KW - Nonparametric spectral estimation methods
KW - Synthetic aperture radar tomography (TomoSAR)
KW - Urban building reconstruction
KW - ITC-ISI-JOURNAL-ARTICLE
KW - 22/1 OA procedure
U2 - 10.1016/j.asr.2021.07.043
DO - 10.1016/j.asr.2021.07.043
M3 - Article
AN - SCOPUS:85113281099
SN - 0273-1177
VL - 69
SP - 1743
EP - 1751
JO - Advances in space research
JF - Advances in space research
IS - 4
ER -