Comparison of density and positioning accuracy of PS extracted from super-resolution PSI with those from traditional PSI

doi:10.23919/JSEE.2021.000111

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (6): 1318-1324.doi: 10.23919/JSEE.2021.000111

收稿日期:2021-05-07 出版日期:2022-01-05 发布日期:2022-01-05

Comparison of density and positioning accuracy of PS extracted from super-resolution PSI with those from traditional PSI

Hao ZHANG¹(), Bin CUI^1,²(), Zhichao GUAN^3,*(), Han DUN⁴()

¹ School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210049, China
² Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China
³ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
⁴ Department of Geoscience and Remote Sensing, Delft University of Technology, Delft 2628, the Netherlands

Received:2021-05-07 Online:2022-01-05 Published:2022-01-05
Contact: Zhichao GUAN E-mail:haozhang@njupt.edu.cn;bincui@njupt.edu.cn;guanzhichao@whu.edu.cn;h.dun@tudelft.nl
About author:|ZHANG Hao was born in 1986. He received his B.S. in remote sensing and information engineering from Wuhan University in 2010, master’s degree in geodetection and information technology from China University of Geosciences (Wuhan) in 2016, and Ph.D. degree from the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China in 2020. He was a visiting Ph.D. student with the Department of Geoscience and Remote Sensing, Delft University of Technology, the Netherlands, from 2017 to 2019. Currently, he is a lecturer in Nanjing University of Posts and Telecommunications. His research interests include new processing algorithms for SAR/InSAR. E-mail: haozhang@njupt.edu.cn||CUI Bin was born in 1989. He received his M.S. degree from the School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture in 2015 and Ph.D. degree from the School of Geodesy and Geomatics, Wuhan University, China in 2020. He is currently a lecturer at the School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing. His research interests include remote sensing data processing and deep learning. E-mail: bincui@njupt.edu.cn||GUAN Zhichao was born in 1990. He received his M.S. degree from China University of Geosciences, Wuhan in 2014, and Ph.D. degree from the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China in 2018. He was engaged in scientific research at the postdoctoral research station, Wuhan University, from 2018 to 2021. His research interests include satellite remote sensing data processing. E-mail: guanzhichao@whu.edu.cn||DUN Han was born in 1991. He received his B.S. degree in communication engineering and M.S. degree in communication and information engineering from Shanghai University, China, in 2013 and 2016, respectively. From 2013 to 2016, he also worked at the Key Laboratory of Specialty Fiber Optics and Optical Access Network in Shanghai University. He is currently pursuing his Ph.D. degree in the Department of Geoscience and Remote Sensing, Delft University of Technology, the Netherlands. His research interests include wireless localization, and statistical signal processing. E-mail: h.dun@tudelft.nl
Supported by:
This work was supported by the National Natural Science Foundation of China (62101284), the State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources, China Academy of Surveying and Mapping (2021-03-11), the Natural Science Project of Jiangsu Province (21KJB420003), Nanjing University of Posts and Telecommunications Start-up Fund (NY221033; NY220168), the Foundation of Jiangsu Province Shuangchuang Doctor Grant (JSSCBS20210543), and Beijing Key Laboratory of Urban Spatial Information Engineering (20210215)

摘要/Abstract

Abstract:

In the application of persistent scatterer interferometry (PSI), deformation information is extracted from persistent scatterer (PS) points. Thus, the density and position of PS points are critical for PSI. To increase the PS density, a time-series InSAR chain termed as “super-resolution persistent scatterer interferometry” (SR-PSI) is proposed. In this study, we investigate certain important properties of SR-PSI. First, we review the main workflow and dataflow of SR-PSI. It is shown that in the implementation of the Capon algorithm, the diagonal loading (DL) approach should be only used when the condition number of the covariance matrix is sufficiently high to reduce the discontinuities between the joint images. We then discuss the density and positioning accuracy of PS when compared with traditional PSI. The theory and experimental results indicate that SR-PSI can increase the PS density in urban areas. However, it is ineffective for the rural areas, which should be an important consideration for the engineering application of SR-PSI. Furthermore, we validate that the positioning accuracy of PS can be improved by SR-PSI via simulations.

Key words: super resolution, persistent scatterer interferometry (PSI), positioning accuracy

. [J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1318-1324.

Hao ZHANG, Bin CUI, Zhichao GUAN, Han DUN. Comparison of density and positioning accuracy of PS extracted from super-resolution PSI with those from traditional PSI[J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1318-1324.

图/表 7

参考文献 25

1	BIANCHINI S, PRATESI F, NOLESINI T Building deformation assessment by means of persistent scatterer interferometry analysis on a landslide-affected area: the Volterra (Italy) case study. Remote Sensing, 2015, 7 (4): 4678- 4701. doi: 10.3390/rs70404678
2	FERRETTI A, PRATI C, ROCCA F Permanent scatterers in SAR interferometry. IEEE Trans. on Geoscience and Remote Sensing, 2001, 39 (1): 8- 20. doi: 10.1109/36.898661
3	BLANCO-SANCHEZ P, MALLORQUI J J, DUQUE S The coherent pixels technique (CPT): an advanced DInSAR technique for nonlinear deformation monitoring. Pure and Applied Geophysics, 2008, 165 (6): 1167- 1193. doi: 10.1007/s00024-008-0352-6
4	CROSETTO M, BIESCAS E, DURO J, et al Generation of advanced ERS and Envisat interferometric SAR products using the stable point network technique. Photogrammetric Engineering and Remote Sensing, 2008, 74 (4): 443- 450. doi: 10.14358/PERS.74.4.443
5	BERARDINO P, FORNARO G, LANARI R, et al A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans. on Geoscience and Remote Sensing, 2002, 40 (11): 2375- 2383. doi: 10.1109/TGRS.2002.803792
6	MORA O, MALLORQUI J J, BROQUETAS A Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images. IEEE Trans. on Geoscience and Remote Sensing, 2003, 41 (10): 2243- 2253. doi: 10.1109/TGRS.2003.814657
7	HOOPER A, ZEBKER H, SEGALL P, et al A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophysical Research Letters, 2004, 31 (23): 1- 5.
8	HOOPER A, ZEBKER H A Phase unwrapping in three dimensions with application to InSAR time series. Journal of the Optical Society of America A, 2007, 24 (9): 2737- 2747. doi: 10.1364/JOSAA.24.002737
9	FERRETTI A, FUMAGALLI A, NOVALI F, et al A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Trans. on Geoscience and Remote Sensing, 2011, 49 (9): 3460- 3470. doi: 10.1109/TGRS.2011.2124465
10	PERISSIN D, WANG T Repeat-pass SAR interferometry with partially coherent targets. IEEE Trans. on Geoscience and Remote Sensing, 2012, 50 (1): 271- 280. doi: 10.1109/TGRS.2011.2160644
11	DEGRAAF S R SAR imaging via modern 2D spectral estimation methods. IEEE Trans. on Image Processing, 1998, 7 (5): 729- 761. doi: 10.1109/83.668029
12	ZHANG H, LOPEZ-DEKKER P Persistent scatterer densification through the application of Capon- and APES-based SAR reprocessing algorithms. IEEE Trans. on Geoscience and Remote Sensing, 2019, 57 (10): 7521- 7533. doi: 10.1109/TGRS.2019.2913905
13	GOODMAN J W. Introduction to Fourier optics. New York: McGraw-Hill, 1996.
14	BORISON S, STEPHEN B B, KEVIN M C Super-resolution methods for wideband radar. Journal of Lincoln Lab, 1992, 5 (3): 441- 461.
15	WANG Z M, WANG W W Fast and adaptive method for SAR superresolution imaging based on point scattering model and optimal basis selection. IEEE Trans. on Image Processing, 2009, 18 (7): 1477- 1486. doi: 10.1109/TIP.2009.2017327
16	ZHU X X, BAMLER R Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR. IEEE Trans. on Geoscience and Remote Sensing, 2012, 50 (1): 247- 258. doi: 10.1109/TGRS.2011.2160183
17	SHEN H F, LIN L P, LI J, et al A residual convolutional neural network for polarimetric SAR image super-resolution. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161, 90- 108. doi: 10.1016/j.isprsjprs.2020.01.006
18	CAPON J High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE, 1969, 57 (8): 1408- 1418. doi: 10.1109/PROC.1969.7278
19	LI J, STOICA P An adaptive filtering approach to spectral estimation and SAR imaging. IEEE Trans. on Signal Processing, 1996, 44 (6): 1469- 1484. doi: 10.1109/78.506612
20	DU L, LI J, STOICA P Fully automatic computation of diagonal loading levels for robust adaptive beamforming. IEEE Trans. on Aerospace and Electronic Systems, 2010, 46 (1): 449- 458. doi: 10.1109/TAES.2010.5417174
21	YAGUE-MARTINEZ N, PRATS-IRAOLA P, RODRIGUEZ GONZALEZ F, et al Interferometric processing of Sentinel-1 TOPS data. IEEE Trans. on Geoscience and Remote Sensing, 2016, 54 (4): 2220- 2234. doi: 10.1109/TGRS.2015.2497902
22	ZHANG H, LOPEZ-DEKKER P. Persistent scatterer densification through Capon based SAR reprocessing for Sentinel-1 TOPS data. IEEE Geoscience and Remote Sensing Letters, 2021. DOI: 10.1109/LGRS.2020.3048370.
23	ZHANG H, LOPEZ-DEKKER P, LI S Combination of super-resolution PSI and traditional PSI by identification of homogeneous areas. IEEE Access, 2020, 8, 181640- 181649. doi: 10.1109/ACCESS.2020.3028491
24	LOPES A, TOUZI R, NEZRY E Adaptive speckle filters and scene heterogeneity. IEEE Trans. on Geoscience and Remote Sensing, 1990, 28 (6): 992- 1000. doi: 10.1109/36.62623
25	DHEENATHAYALAN P, SMALL D, SCHUBERT A, et al. High-precision positioning of radar scatterers. Journal of Geodesy, 2016, 90(5): 403–422.

Comparison of density and positioning accuracy of PS extracted from super-resolution PSI with those from traditional PSI

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 25

相关文章 0

编辑推荐

Metrics

本文评价