Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT)
The application of SAR tomography (TomoSAR) on the urban infrastructure and other man-made buildings has gained increasing popularity with the development of modern high-resolution spaceborne satellites. Urban tomography focuses on the separation of the overlaid targets within one azimuth-range reso...
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doaj-904626088afe452e91e293147c45bc9e2020-11-24T22:12:41ZengMDPI AGRemote Sensing2072-42922019-08-011116193010.3390/rs11161930rs11161930Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT)Hui Luo0Zhenhong Li1Zhen Dong2Anxi Yu3Yongsheng Zhang4Xiaoxiang Zhu5College of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaCOMET, School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UKCollege of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science, National University of Defense Technology, Changsha 410073, ChinaThe application of SAR tomography (TomoSAR) on the urban infrastructure and other man-made buildings has gained increasing popularity with the development of modern high-resolution spaceborne satellites. Urban tomography focuses on the separation of the overlaid targets within one azimuth-range resolution cell, and on the reconstruction of their reflectivity profiles. In this work, we build on the existing methods of compressive sensing (CS) and generalized likelihood ratio test (GLRT), and develop a multiple scatterers detection method named CS-GLRT to automatically recognize the number of scatterers superimposed within a single pixel as well as to reconstruct the backscattered reflectivity profiles of the detected scatterers. The proposed CS-GLRT adopts a two-step strategy. In the first step, an L1-norm minimization is carried out to give a robust estimation of the candidate positions pixel by pixel with super-resolution. In the second step, a multiple hypothesis test is implemented in the GLRT to achieve model order selection, where the mapping matrix is constrained within the afore-selected columns, namely, within the candidate positions, and the parameters are estimated by least square (LS) method. Numerical experiments on simulated data were carried out, and the presented results show its capability of separating the closely located scatterers with a quasi-constant false alarm rate (QCFAR), as well as of obtaining an estimation accuracy approaching the Cramer−Rao Low Bound (CRLB). Experiments on real data of Spotlight TerraSAR-X show that CS-GLRT allows detecting single scatterers with high density, distinguishing a considerable number of double scatterers, and even detecting triple scatterers. The estimated results agree well with the ground truth and help interpret the true structure of the complex or buildings studied in the SAR images. It should be noted that this method is especially suitable for urban areas with very dense infrastructure and man-made buildings, and for datasets with tightly-controlled baseline distribution.https://www.mdpi.com/2072-4292/11/16/1930SAR tomography (TomoSAR)Compressive sensing (CS)Generalized Likelihood Ratio Test (GLRT)super-resolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hui Luo Zhenhong Li Zhen Dong Anxi Yu Yongsheng Zhang Xiaoxiang Zhu |
spellingShingle |
Hui Luo Zhenhong Li Zhen Dong Anxi Yu Yongsheng Zhang Xiaoxiang Zhu Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT) Remote Sensing SAR tomography (TomoSAR) Compressive sensing (CS) Generalized Likelihood Ratio Test (GLRT) super-resolution |
author_facet |
Hui Luo Zhenhong Li Zhen Dong Anxi Yu Yongsheng Zhang Xiaoxiang Zhu |
author_sort |
Hui Luo |
title |
Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT) |
title_short |
Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT) |
title_full |
Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT) |
title_fullStr |
Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT) |
title_full_unstemmed |
Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT) |
title_sort |
super-resolved multiple scatterers detection in sar tomography based on compressive sensing generalized likelihood ratio test (cs-glrt) |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-08-01 |
description |
The application of SAR tomography (TomoSAR) on the urban infrastructure and other man-made buildings has gained increasing popularity with the development of modern high-resolution spaceborne satellites. Urban tomography focuses on the separation of the overlaid targets within one azimuth-range resolution cell, and on the reconstruction of their reflectivity profiles. In this work, we build on the existing methods of compressive sensing (CS) and generalized likelihood ratio test (GLRT), and develop a multiple scatterers detection method named CS-GLRT to automatically recognize the number of scatterers superimposed within a single pixel as well as to reconstruct the backscattered reflectivity profiles of the detected scatterers. The proposed CS-GLRT adopts a two-step strategy. In the first step, an L1-norm minimization is carried out to give a robust estimation of the candidate positions pixel by pixel with super-resolution. In the second step, a multiple hypothesis test is implemented in the GLRT to achieve model order selection, where the mapping matrix is constrained within the afore-selected columns, namely, within the candidate positions, and the parameters are estimated by least square (LS) method. Numerical experiments on simulated data were carried out, and the presented results show its capability of separating the closely located scatterers with a quasi-constant false alarm rate (QCFAR), as well as of obtaining an estimation accuracy approaching the Cramer−Rao Low Bound (CRLB). Experiments on real data of Spotlight TerraSAR-X show that CS-GLRT allows detecting single scatterers with high density, distinguishing a considerable number of double scatterers, and even detecting triple scatterers. The estimated results agree well with the ground truth and help interpret the true structure of the complex or buildings studied in the SAR images. It should be noted that this method is especially suitable for urban areas with very dense infrastructure and man-made buildings, and for datasets with tightly-controlled baseline distribution. |
topic |
SAR tomography (TomoSAR) Compressive sensing (CS) Generalized Likelihood Ratio Test (GLRT) super-resolution |
url |
https://www.mdpi.com/2072-4292/11/16/1930 |
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