Modified Statistically Homogeneous Pixels’ Selection With Multitemporal SAR Images

Wang, Yingjie; Deng, Yunkai; Fei, Wenbo; Wang, Robert; Song, Huina; Wang, Jili; Li, Ning

doi:10.1109/lgrs.2016.2618766

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…The performance of PCPWPL was tested with comparison to the conventional amplitude-based method. The AD test has been proved to outperform other alternative algorithms [48,49], therefore we chose the AD test as the representative for the amplitude-based clustering strategy. Three different strategies were therefore designed for the comparison in their computational efficiency.…”

Section: Simulated Experimentsmentioning

confidence: 99%

Potential of Using Phase Correlation in Distributed Scatterer InSAR Applied to Built Scenarios

Shi

Lin

et al. 2020

Remote Sensing

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The improved spatial resolution of Synthetic Aperture Radar (SAR) images from newly launched sensors has promoted a more frequent use of distributed scatterer (DS) interferometry (DSI) in urban monitoring, pursuing sufficient and detailed measurements. However, the commonly used statistical methods for homogeneous pixel clustering by exploring amplitude information are firstly, computationally intensive; furthermore, their necessity when applied to high-coherent built scenarios is little discussed in the literature. This paper explores the potential of using phase information for the detection of homogeneous pixels on built surfaces. We propose a simple phase-correlated pixel (PCP) clustering and introduce a coherence-weighted phase link (WPL), i.e., PCPWPL, to pursue a faster processing of interferogram phase denoising. Rather than relying on the statistical tests of amplitude characteristics, we exploit vector correlation in the complex domain to identify PCPs with similar phase observations, thus, avoiding the intensive hypothesis test. A coherence-weighted phase linking is applied for DS phase reconstruction. The estimation of geophysical parameters, e.g., deformation, is completed using an integrated network of persistent scatterers (PS) and DS. Efficiency of the proposed method is fairly illustrated by both synthetic and real data experiments. Pros and cons of the proposed PCPWPL were analyzed with the comparison to a conventional amplitude-based strategy using an X-band CosmoSkyMed dataset. It is demonstrated that the use of phase correlation is sufficient for DS monitoring in built scenarios, with equivalent measurement quantity and cheaper computational cost.

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Section: Simulated Experimentsmentioning

confidence: 99%

Potential of Using Phase Correlation in Distributed Scatterer InSAR Applied to Built Scenarios

Shi

Lin

et al. 2020

Remote Sensing

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show abstract

“…To perform the statistical test analysis, we considered the AD test, which is a statistical nonparametric hypothesis test generally used to investigate whether a given probability distribution null hypothesis H 0 yields a good fit for a given sample data [20], [21]. The AD goodness-of-fit test measures the distance A n between the empirical distribution function F Y (y) and the cumulative distribution function under the null hypothesis F 0 (y) as [22]:…”

Section: A Anderson-darling Goodness-of-fit Testmentioning

confidence: 99%

Computer-Simulated UWB VHF SAR Targets Based on Statistics of Real Data

Palm¹,

Alves²,

Machado³

et al. 2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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In this paper, we performed a statistical study of the targets of wavelength-resolution synthetic aperture radar (SAR) images and addressed the problem of limited training samples. Based on suitable statistical models for the real targets of the evaluated SAR image, we considered a sample generator method to create computer-simulated targets. For such, well-known statistical models, namely, Burr, Gamma, Gaussian, Log-normal, Rayleigh, Rician, and Weibull, are considered for the Anderson-Darling (AD) goodness-of-fit test. This test is a statistical nonparametric hypothesis test generally used to investigate whether a given probability distribution yields a good fit for a given data set. The images used in this study were obtained from the Swedish ultrawideband (UWB) very-high-frequency (VHF) SAR CARABAS II system whose consists of 24 magnitude single-look SAR images. The obtained computer-simulated targets followed similar statistical characteristics when compared to the real ones.

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“…The study of the Earth's surface displacements through differential SAR interferometry represents a consolidated practice [1][2][3][4] at present. In this framework, the last few decades have seen the flourishing of several multi-temporal InSAR (Mt-InSAR) algorithms for the generation of ground displacement time series [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. They can be broadly grouped into two main categories: persistent scatterer (PS) and small baseline (SB) methodologies.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Groundwater Depletion/Inflation and Freeze–Thaw Cycles in the Northern Urumqi Region with the SBAS Technique and an Adjusted Network of Interferograms

et al. 2021

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This work investigated the large-scale ground deformations threatening the Northern Urumqi district, China, which are connected to groundwater exploitation and the seasonal freeze–thaw cycles that characterize this frozen region. Ground deformations can be well captured by satellite data using a multi-temporal interferometric synthetic aperture radar (Mt-InSAR) approach. The accuracy of the achievable ground deformation products (e.g., mean displacement time series and related ground displacement time series) critically depends on the number and quality of the selected interferograms. This paper presents a straightforward interferogram selection algorithm that can be applied to identify an optimal network of small baseline (SB) interferograms. The selected SB interferograms are then used to produce ground deformation products using the well-known small baseline subset (SBAS) Mt-InSAR algorithm. The developed interferogram selection algorithm (ISA) permits the selection of the group of SB data pairs that minimize the relative error of the mean ground deformation velocity. Experiments were carried out using a group of 102 Sentinel-1B SAR data collected from 12 April 2017 to 29 October 2020. This research study shows that the investigated farmland region is characterized by a maximum ground deformation rate of about 120 mm/year. Periodic groundwater overexploitation, coupled with irrigation and freeze–thaw phases, is also responsible for seasonal (one-year) ground displacement signals, with oscillation amplitudes up to 120 mm in the zones of maximum displacement.

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Modified Statistically Homogeneous Pixels’ Selection With Multitemporal SAR Images

Cited by 15 publications

References 21 publications

Potential of Using Phase Correlation in Distributed Scatterer InSAR Applied to Built Scenarios

Potential of Using Phase Correlation in Distributed Scatterer InSAR Applied to Built Scenarios

Computer-Simulated UWB VHF SAR Targets Based on Statistics of Real Data

Analysis of Groundwater Depletion/Inflation and Freeze–Thaw Cycles in the Northern Urumqi Region with the SBAS Technique and an Adjusted Network of Interferograms

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