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Original Articles

Urban landscape monitoring based on high-resolution spaceborne TerraSAR-X data: a case study of Nanjing City, China

, , , &
Pages 447-456 | Received 30 Aug 2017, Accepted 29 Jan 2018, Published online: 06 Feb 2018
 

ABSTRACT

Recently, the performance of high-resolution spaceborne synthetic aperture radar (SAR) data for urban landscape monitoring has attracted increased interest, in particular for regions where rainy weather prevails throughout of year. In this study, the phase component of SAR data was exploited for landscape monitoring in the western area of Nanjing city, China. Twenty-six scenes of TerraSAR-X single-look complex (SLC) interferometric images were acquired for the period of May 2013 to February 2015 and applied for urban change detection as well as surface subsidence monitoring by means of interferometric SAR (InSAR) technologies. Firstly, a small baseline strategy in conjunction with a fast statistically homogeneous pixel selection (FaSHPS) approach were applied for the coherence imagery generation and for the coherent change detection, resulting in a correct probability of better than 90%. Second, the surface deformation was derived with millimetre accuracy using an enhanced multi-temporal InSAR (MT-InSAR) tool by synergistically analyzing the amplitudes and phases of the SAR acquisitions. This study indicates that high-resolution SAR data have the potential for urban landscape monitoring by taking advantage of the interferometric performance and the all-day and all-weather imaging capability.

Disclosure statement

We declare that there are no conflicts of interest in the research.

Additional information

Funding

This research was supported by funding from the National Key Research and Development Program of China (No. 2016YFB0501502), National Natural Science Foundation of China (NSFC) (No. 41771489) and Hundred Talents Program of the Chinese Academy of Sciences (CAS) (Y5YR0300QM). TerraSAR-X data in this study were provided by Deutschen Zentrums für Luft- und Raumfahrt (DLR) under the AO TSX-Archive (CAL3304).

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