Persistent Scatterer Densification Through the Application of Capon- and APES-Based SAR Reprocessing Algorithms

Zeng, Hao; López‐Dekker, Paco

doi:10.1109/tgrs.2019.2913905

Cited by 6 publications

(14 citation statements)

References 26 publications

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“…A detailed discussion of SR-PSI has been presented in [10]. By applying the Capon-based reprocessing algorithm, it obtains the stack of super-resolution images.…”

Section: B Combination Of Sr-psi and Traditional Psi With Cv-indexmentioning

confidence: 99%

“…The PS density is roughly proportional to the bandwidth of the SAR system (the wider the bandwidth, the finer the resolution) [5], thus data from higher-resolution SAR systems such as TerraSAR-X [6], [7] and Cosmo-Skymed [8], [9] provides higher PS density. A more feasible and economical approach, though, is through the application of super-resolution reprocessing algorithms [10]. The corresponding processing chain, which is called Super-Resolution PSI (SR-PSI), is also proposed by the reference [10].…”

Section: Introductionmentioning

confidence: 99%

“…A more feasible and economical approach, though, is through the application of super-resolution reprocessing algorithms [10]. The corresponding processing chain, which is called Super-Resolution PSI (SR-PSI), is also proposed by the reference [10]. Compared with the regular PSI chain, the main change of SR-PSI is that it involves the Super-Resolution reprocessing algorithms and applies the peakdetection-based Persistent Scatterer Candidate (PSC) selection method.…”

Section: Introductionmentioning

confidence: 99%

“…The idea of SR-PSI comes from the applications of modern spectral estimation algorithms in improving the resolution and suppressing sidelobe levels [11]- [13]. A range of spectral estimation algorithms are available, and the Capon algorithm seems to be the most promising one concerning PSI [10]. Thus we only focus on the Capon algorithm in SR-PSI.…”

Section: Introductionmentioning

confidence: 99%

“…For the homogeneous areas, we select PSCs from the original images. For the non-homogeneous ones, we run the Capon-based reprocessing algorithm on the chip images, followed by a peak-detection based PSC selection approach [10]. We combine PSCs in both cases for further PSI processing, mainly including PSC network construction, temporal ambiguity resolution, spatial ambiguity resolution and atmosphere phase estimation.…”

Section: Introductionmentioning

confidence: 99%

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Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous Areas

Zhang

López‐Dekker

2020

IEEE Access

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The performance of Persistent Scatterer Interferometry (PSI) depends heavily on Persistent Scatterer (PS) density. In order to increase PS density, we can apply Super-Resolution reprocessing algorithms in PSI. Involving the reprocessing algorithms and the peak-detection-based Persistent Scatterer Candidate points (PSCs) selection method, the full PSI chain is referred to as Super-Resolution PSI (SR-PSI). The implementation of the Super-Resolution reprocessing algorithm, however, is computationally intensive, which makes SR-PSI time-consuming. In this work, we propose to improve the efficiency by constraining the Capon-based reprocessing to the non-homogeneous areas (e.g., urban areas). We notice that the Capon algorithm performs similarly as the Fourier-based algorithm for homogeneous regions (e.g., grassland), thus we can use Single Look Complex (SLC) images for these areas. With the Coefficient of Variation (CV) as the index, we divide the full image into two classes: homogeneous areas, for which we select PSCs from the original stack, and non-homogeneous areas, for which we extract PSCs from the Capon-based reprocessed images. Then we combine the PSCs of both cases for further PSI processing. We applied the combination method to a stack of TerraSAR-X data. The results show that the proposed approach is more computationally efficient than the original SR-PSI with the effectiveness uncompromised, especially for applications aiming at the urban deformation.

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“…A detailed discussion of SR-PSI has been presented in [10]. By applying the Capon-based reprocessing algorithm, it obtains the stack of super-resolution images.…”

Section: B Combination Of Sr-psi and Traditional Psi With Cv-indexmentioning

confidence: 99%