An improved distributed scatterers extraction algorithm for monitoring tattered ground surface subsidence with DSInSAR: A case study of loess landform in Tongren county

Bao, Jiawen; Luo, X.J.; Liu, Guoxiang; Chang, Ling; Wang, Xiaowen; Shi, Yueling; Wu, Shengbao

doi:10.1016/j.jag.2021.102322

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…In general, the pixels with more than 20 SHPs are selected as DS candidates in order to preserve PS information [5], [28]. So we set a 55  window closer to SHP=20 [6]. The position of a small 55  window in a 15 15  window is shown in Fig.…”

Section: Phase Quality Evaluationmentioning

confidence: 99%

“…However, in low coherence regions that are dominated by farmland, woodland and mountainous areas, Small Baseline Subset (SBAS) [4] and SqueeSAR [5] Manuscript Youfeng Liu, Honglei Yang and Zhaowei Lu are with the School of Land Science and Technology, China University of Geosciences, Beijing, 100083, methods are used. In recent years, the DSInSAR technique, developed on the basis of the SqueeSAR method, has been increasingly used in low coherence regions [6]- [8]. In the low coherence region, the feature backscatter is low and lacks high temporal coherence points.…”

Section: Introduction Ime Series Insar Technology Has Been Increasinglymentioning

confidence: 99%

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NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

Liu

Yang

Jiang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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When the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) technology is used for surface deformation monitoring, the accuracy strongly depends on the quality of phase optimization. Especially in the low coherence region, how to conveniently and effectively improve the quality of phase optimization has been a hot and difficult research topic in recent years. This paper proposes a hybrid phase optimization method for the DSInSAR technology, which chooses either non-local (NL) or minimum mean square error (MMSE) method for each pixel according to the distribution of statistically homogeneous pixel (SHP) in two windows of different sizes. This hybrid method (NL-MMSE) is not limited by the number of interferogram and is not influenced by multi-master images. The NL-MMSE method was applied to the deformation monitoring in the Jinsha River basin, Tibet, China, using 28 Sentinel-1A SAR images acquired between February and December 2020. Compared with the NL and MMSE methods, the NL-MMSE method provides better phase quality of the interferogram stack and is able to extract more temporal coherence points for subsequent deformation inversion. The deformation monitoring results showed there are three obvious large-scale landslide deformation and dozens of small-scale deformation in the study area. It is demonstrated that the NL-MMSE method based on DSInSAR technology can accurately monitor the detailed deformation characteristics of the low coherence surface, and can be applied and promoted as an effective means of identifying and monitoring geological hazards.

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Section: Phase Quality Evaluationmentioning

confidence: 99%

Section: Introduction Ime Series Insar Technology Has Been Increasinglymentioning

confidence: 99%

NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

Liu

Yang

Jiang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In the 1970s, due to the introduction of interferometric technology, two synthetic aperture radar (SAR) images covering the same area were jointly processed [16], and the corresponding phase difference was extracted to restore the target shape, such as the establishment of a digital elevation model, leading to the development of InSAR technology [17]. After decades of development in InSAR technology, differential interferometric synthetic aperture radar (D-InSAR) [18,19], small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) [20], persistent scatterer interferometric synthetic aperture radar (PS-InSAR) [21], and distributed scatterer interferometric synthetic aperture radar (DS-InSAR) [22] techniques have been proposed, and these techniques have been widely used and can be employed to monitor land subsidence [23,24]. InSAR technology can be adopted to monitor land subsidence caused by mining with low cost and high efficiency [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A New Inversion Method for Obtaining Underwater Spatial Information of Subsidence Waterlogging Based on InSAR Technology and Subsidence Prediction

Zhu,

Qiu,

Zhang

et al. 2024

Water

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Surface waterlogging disasters due to underground mining and geological status have caused the abandonment of fertile land, seriously damaged the ecological environment, and have influenced the sustainable development of coal resource-based cities, which has become a problem that some mining areas need to face. However, the traditional underwater terrain measurement method using sonar encompasses a time-consuming and labor-intensive process. Thus, an inversion method for obtaining the underwater spatial information of subsidence waterlogging in coal mining subsidence waterlogging areas is proposed, based on differential interferometric synthetic aperture radar (D-InSAR) and the probability integral prediction method. First, subsidence values are obtained in the marginal area of the subsidence basin using D-InSAR technology. Then, the subsidence prediction parameters of the probability integral method (PIM) are inverted by a genetic algorithm (GA) based on the subsidence values. Finally, the underwater spatial information of subsidence waterlogging is calculated on the basis of the prediction parameters. The subsidence waterlogging area in the Wugou coal mine was adopted as the study area, and the underwater spatial information of subsidence waterlogging was inverted by the proposed method. The results show that this method can effectively provide the underwater spatial information of subsidence waterlogging, including the maximum subsidence value, waterlogging volume, subsidence waterlogging area, and underwater terrain in the subsidence waterlogging area. Compared with field-measured data from the same period, the RMSE of water depth is 99 mm, and the relative error is 9.9%, which proves that this inversion method is accurate and can meet engineering precision requirements.

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“…Similarity between a central pixel and its surroundings is estimated by the Kolmogorov-Smirnov (K-S) test [29]. To overcome the computational burden of DespecKS, the fast SHP selection (FaSHPs) [25] and its variant [30] were developed. They construct a confidence interval for each pixel using the amplitude images and select SHPs according to that interval.…”

mentioning

confidence: 99%

Despeckling Multitemporal Polarimetric SAR Data Based on Tensor Decomposition

Luo

Zhang

Dong

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Despeckling is an essential task in polarimetric synthetic aperture radar (PolSAR) image processing. Most existing filters developed for multi-temporal SAR images make use of either real or complex information. Real information refers to amplitude or intensity values, whereas complex information refers to complex covariance matrix (CCM) derived from either interferometric SAR (InSAR) or PolSAR data. The InSAR CCM is formed using images of the same polarimetric channel but acquired at different dates, and the PolSAR CCM contains information acquired simultaneously in different polarimetric channels. Therefore, these despeckling methods may present a good performance in some applications and scenes but fail in other cases, due to differences in input sources. In order to achieve a more robust result in all cases, we develop a method for multi-temporal polarimetric SAR data filtering based on tensor decomposition (TD-MPF), which aims at improving the identification of homogeneous pixels for spatially adaptive filtering. The key element of this approach consists of exploiting tensor theory to construct a new CCM that contains both polarimetric and interferometric information, as well as multi-temporal information for each pixel. The effectiveness of the proposed method and its performance are evaluated with simulated and real SAR data in comparison with several established methods.

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An improved distributed scatterers extraction algorithm for monitoring tattered ground surface subsidence with DSInSAR: A case study of loess landform in Tongren county

Cited by 10 publications

References 21 publications

NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

A New Inversion Method for Obtaining Underwater Spatial Information of Subsidence Waterlogging Based on InSAR Technology and Subsidence Prediction

Despeckling Multitemporal Polarimetric SAR Data Based on Tensor Decomposition

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