PS Selection Method for and Application to GB‐SAR Monitoring of Dam Deformation

Xiang, Xia; Chen, Jiankang; Wang, Hui; Peng, Liang; Wu, Zhenyu

doi:10.1155/2019/8320351

Cited by 9 publications

(7 citation statements)

References 51 publications

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“…GB-SAR(ground based synthetic aperture radar) early warning can warn 10 minutes earlier for landslides . Combination of UAV and GB-SAR will be more beneficial' [18][19] Satellite images and convolutional neural network (CNN) can be used to identification and classification of landslide . A new method of distant domain transfer learning (DDTL) were introduced for detection and classification of landslides.…”

Section: Slidementioning

confidence: 99%

A prototype model for detection and classification of landslides using satellite data

Sharma

Sapate³

2022

J. Phys.: Conf. Ser.

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Landslides are natural and manmade disasters that cause threat to human life and lead to huge economic loss. Last few decade number of approaches have been developed for early detection of landslide for protecting life and saving properties. This paper proposes a prototype for an artificial intelligent model to detect and predict different types of landslides in hilly area with remote sensing techniques. All developing countries are following a steep increase in development of infrastructure like buildings, roads tunnels bridges railway tracks. Demand of connecting remote area is very high but on other side of environment it is also true that high demand of construction in morpho material area is causing many disasters like landslide. Landslide causes the loss of property and life so an early alarming will be help full for disaster management. Remotely sensed data pre-processed with artificial intelligent technologies will be helpful for landslide detection, creating landslide susceptibility map and inventory. Focus of this study is on enhancing the accuracy to detect landslide, list out the different features for extraction from satellite images and pre processing steps. This research also focuses on application of robust early prediction of type of landslide. This research will help in detection of landslide early to protect economical losses and human lives.

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Section: Slidementioning

confidence: 99%

A prototype model for detection and classification of landslides using satellite data

Sharma

Sapate³

2022

J. Phys.: Conf. Ser.

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“…After obtaining the PS in the monitoring area according to the PS selection method (Xiang et al, 2019), the Delaunay triangle network construction principle can be adopted to establish PS triangle network. As shown in Figure 2, if there are N PS points in an image, M PS edges are formed according to the Delaunay triangle network construction principle, then M double-difference phases can be obtained (i.e.…”

Section: Space Phase Unwrappingmentioning

confidence: 99%

An improved method of GB-SAR phase unwrapping for landslide monitoring

Xiang

Chen²,

Wang³

et al. 2022

Front. Earth Sci.

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The ground-based Synthetic Aperture Radar (GB-SAR) technique can be applied to the safety monitoring and early warning of geo-hazards, especially for monitoring displacement of various types of landslide masses. One of the key techniques of processing GB-SAR monitoring data is phase unwrapping, which can be dramatically affected by atmospheric humidity, atmospheric pressure, sampling interval, etc. In high mountains and valleys where environmental change is drastic, GB-SAR monitoring data is vulnerable to incoherence both spatially and temporally. Therefore, an improved phase unwrapping method of GB-SAR data for landslide monitoring is proposed in this paper, which can realize three-dimensional phase unwrapping in time and space. The method adopts the idea of sparse data processing and realizes phase unwrapping of monitoring data in two steps. Firstly, taking full advantage of the high temporal resolution of GB-SAR monitoring data, the one-dimensional phase unwrapping method in the spatial domain is applied to the time domain, and the interference phases of PS points are unwrapped in the time domain. Then, the PS network is constructed based on the time-phase unwrapping results, the double-difference phases of the network baselines are obtained and the spatial consistency adjustment is applied to them, and the spatial phase unwrapping can be realized by the indirect least square adjustment method. This method successfully solves the problem of path dependence, island isolation and large computation of the common method of space-borne SAR phase unwrapping, which can process GB-SAR data with high accuracy and efficiency.

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“…In addition, some unstable or even incorrect PSs in specific scenes were selected with these methods. Recently, some researchers, both domestically and abroad [ 17 , 18 , 19 ], have combined conventional methods with the relevant pixel information. Some improved multi-threshold PS selection methods based on the conventional methods proposed.…”

Section: Introductionmentioning

confidence: 99%

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images

Tan,

Li,

Hou

et al. 2024

Sensors

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The micro-deformation monitoring radar is usually based on Permanent Scatterer (PS) technology to realize deformation inversion. When the region is continuously monitored for a long time, the radar image amplitude and pixel variance will change significantly with time. Therefore, it is difficult to select phase-stable scatterers by conventional amplitude deviation methods, as they can seriously affect the accuracy of deformation inversion. For different regions studied within the same scenario, using a PS selection method based on the same threshold often increases the size of the deformation error. Therefore, this paper proposes a new PS selection method based on the Gaussian Mixture Model (GMM). Firstly, PS candidates (PSCs) are selected based on the pixels’ amplitude information. Then, the amplitude deviation index of each PSC is calculated, and each pixel’s probability values in different Gaussian distributions are acquired through iterations. Subsequently, the cluster types of pixels with larger probability values are designated as low-amplitude deviation pixels. Finally, the coherence coefficient and phase stability of low-amplitude deviation pixels are calculated. By comparing the probability values of each of the pixels in different Gaussian distributions, the cluster type with the larger probability, such as high-coherence pixels and high-phase stability pixels, is selected and designated as the final PS. Our analysis of the measured data revealed that the proposed method not only increased the number of PSs in the group, but also improved the stability of the number of PSs between groups.

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PS Selection Method for and Application to GB‐SAR Monitoring of Dam Deformation

Cited by 9 publications

References 51 publications

A prototype model for detection and classification of landslides using satellite data

A prototype model for detection and classification of landslides using satellite data

An improved method of GB-SAR phase unwrapping for landslide monitoring

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images

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