Yixian Tang

Beijing Institute of Big Data Research

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Chao Wang

Beijing Institute of Big Data Research

Hong Zhang

Aerospace Information Research Institute

Zhengjia Zhang

China University of Geosciences

Fan Wu

Aerospace Information Research Institute

Lichuan Zou

Aerospace Information Research Institute

Bo Zhang

Chinese Academy of Sciences

Jing Wang

China Earthquake Administration

Bo Zhang

North China University of Technology

Xuefei Zhang

Ministry of Natural Resources

Longkai Dong

Aerospace Information Research Institute

Cooperative Institutions

Chinese Academy of Sciences

University of Chinese Academy of Sciences

Aerospace Information Research Institute

Beijing Institute of Big Data Research

China University of Mining and Technology

Institute of Remote Sensing and Digital Earth

Wuhan University

Ministry of Natural Resources

Shenzhen University

Institute of Tibetan Plateau Research

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SCGC-Net:Spatial Context Guide Calibration Network for multi-source RSI Landslides detection

IEEE Transactions on Geoscience and Remote Sensing (2025)

Yukun Fan Peifeng Ma Q. Hu Guiwei Liu Zihuan Guo

10.1109/tgrs.2025.3528750

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Citations (0)

A non-supervised method for shoreline extraction using high resolution SAR image

Long Zhao Ling Fan Chao Wang Yixian Tang Bo Zhang

This paper presents a non-supervised method for shoreline extraction from high resolution synthetic aperture radar (SAR) image. The proposed technique is based on level set with all the parameters optimized in order to be used with different kinds of SAR data and to find the desired boundary with a minimize number of iterations so as to be fast enough. The preprocessing including Roberts Operator and Histogram adjusting is used to enhance the contrast of the boundary. Then we initial the curve to cover the whole image, and an improved level set method is used to do the segmentation. After that, several post-processing steps are utilized to remove any remaining spurious segments. It's completely non-supervised and can be applied to different kinds of SAR data. The results confirm that the proposed method can provide a stable and fast solution to the shoreline extraction using SAR data.

Spurious relationship

10.1109/cvrs.2012.6421282

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Citations (2)

Improved SBAS-INSAR Based on Two-Stage Optimal Interferometric Pair Selection for Permafrost Monitoring in the Qinghai-Tibet Plateau

Lichuan Zou Chao Wang Yixian Tang Tianyang Li Shaoyang Guan

Small-Baseline Interferometric Synthetic Aperture Radar (SBAS-InSAR) has been widely used in the field of permafrost deformation monitoring. However, previous research has mainly relied on short-term interferograms, which may cause phase bias or signal fading. In this study, we propose an improved SBAS-InSAR algorithm based on a two-stage optimal interferometric pair selection strategy that integrates both short-term and long-term interferograms. The central Qing-Tibet Plateau, characterized by its high elevation and low vegetation cover, provides an ideal region for permafrost monitoring using InSAR technology. By comparing the results of the SBAS-InSAR technique based on three different interferogram networks, our method can detect 129 additional thawing permafrost areas with local subsidence in the QTP study areas. In contrast, these subsidence areas are missed by SBAS-InSAR based on two other interferogram networks.

GNSS augmentation

Elevation (ballistics)

10.1109/bigsardata59007.2023.10294522

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First mapping of China surface movement using supercomputing interferometric SAR technique

Science Bulletin (2021)

Chao Wang Yixian Tang Hong Zhang Haihang You Weikang Zhang

10.1016/j.scib.2021.04.026

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Citations (24)

Analysis of ground subsidence at a coal-mining area in Huainan using time-series InSAR

International Journal of Remote Sensing (2015)

Zhengjia Zhang Chao Wang Yixian Tang Hong Zhang Qiaoyan Fu

In Huainan City, Anhui Province, China, ground subsidence persistently occurs due to underground coal mining, which has caused several environmental issues. A modified time-series Interfermetric synthetic aperture radar (InSAR) technique is applied to obtain ground movement in Huainan over a period of approximately two years using 20 ascending Radarsat-2 images. In particular, distributed scatterers (DSs) are efficiently identified using classification information and statistical characteristics. Classified information is used to identify the specific DS classes and statistical characteristics are used to refine the DS candidates. To control error propagation and improve computational efficiency, the deformation rate and digital elevation model (DEM) error of persistent scatterers (PSs) are first retrieved using conventional persistent scatterer intermemetry (PSI). Then, a region-growing-based strategy is applied to extract the deformation rate of DSs. Land subsidence is detected in coal-mining areas of central Dingji, Guqiao, Zhangjiaji, Xinji I, and Xinji II, which suggests that the subsidence is primarily caused by underground mining. The fastest subsidence occurring in non-urban areas is particularly vulnerable to subsidence and collapsed lakes. It is also found that the collapsed lakes have expanded during the observation period, with an expanded mining area of 0.842 km2 near Guqiao. A linear relationship between the observed subsidence and expansion of collapsed lakes was found, which indicates that the expansion of collapsed lakes hass resulted from subsidence due to underground coal mining.

Ground subsidence

10.1080/01431161.2015.1109725

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Citations (28)

The comprehensive deformation retrieval for SAR interferometric data

International Journal of Remote Sensing (2012)

Lian Tao H. Zhang Chao Wang Yixian Tang

Abstract This study presents a comprehensive deformation retrieval method based on the multi-baseline synthetic aperture radar interferometry (InSAR) technique for ground movement monitoring. Both the displacement pattern in at the large scale and detailed deformation for local areas are investigated by exploiting two sets of data generated at low and full spatial resolution. In particular, we focus on the description and estimation of non-linear deformation in full-resolution signals. A polynomial parameter model is built and the total least squares-singular value decomposition algorithm is proposed to control the overall errors of the system. The ascending Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) data set on Tianjin China is used for the algorithm experiment and detailed displacement information between July 2008 and August 2010 is obtained. The experimental results are validated by levelling data and field surveys, and the good agreement proves the effectiveness of the algorithm. Acknowledgements This work is supported by the National High-tech Research and Development Programme of China (Grant No. 2009AA12Z118) and the National Natural Science Foundation of China (Grant Nos. 40971198 and 40701106). The authors thank JAXA for the ALOS PALSAR data, and reviewers for improving the article through their patient work.

Levelling

Data set

Deformation monitoring

10.1080/01431161.2012.718456

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Citations (2)

Damage analysis of 2008 Wenchuan earthquake using SAR images

Kai Jiang Chao Wang Hong Zhang Wei Chen Bo Zhang

On May 12, 2008, Wenchuan earthquake (Ms 8.0) occurred in Sichuan, Southwestern China. This catastrophe caused severe damage of constructions in urban and rural areas, and fundamental infrastructure, such as those facilities of factories, electrical power, telecommunication and transportation, etc. The earthquake also resulted in many geological phenomena, i.e. landslide, debris flow, landslide lakes, etc. which also triggered off damage and threat. The airborne campaigns were performed after the earthquake with high-resolution SAR system for rescue and relief effort and damage assessment. In this study, damage of various facilities was described and analyzed using airborne SAR images acquired during the campaigns. The results were verified with ground truth investigation. The results showed the role of SAR data in earthquake damage assessment.

Debris flow

10.1109/igarss.2009.5417722

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Citations (9)

Inversion of seismic source parameters from satellite InSAR data based on deep learning

Tectonophysics (2021)

Xin Zhao Chao Wang Hong Zhang Yixian Tang Bo Zhang

Rake

10.1016/j.tecto.2021.229140

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Citations (9)

Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model

Remote Sensing (2021)

Lu Xu Hong Zhang Chao Wang Sisi Wei Bo Zhang

The elimination of hunger is the top concern for developing countries and is the key to maintain national stability and security. Paddy rice occupies an essential status in food supply, whose accurate monitoring is of great importance for human sustainable development. As one of the most important paddy rice production countries in the world, Thailand has a favorable hot and humid climate for paddy rice growing, but the growth patterns of paddy rice are too complicated to construct promising growth models for paddy rice discrimination. To solve this problem, this study proposes a large-scale paddy rice mapping scheme, which uses time-series Sentinel-1 data to generate a convincing annual paddy rice map of Thailand. The proposed method extracts temporal statistical features of the time-series SAR images to overcome the intra-class variability due to different management practices and modifies the U-Net model with the fully connected Conditional Random Field (CRF) to maintain the edge of the fields. In this study, 758 Sentinel-1 images that covered the whole country from the end of 2018 to 2019 were acquired to generate the annual paddy rice map. The accuracy, precision, and recall of the resultant paddy rice map reached 91%, 87%, and 95%, respectively. Compared to SVM classifier and the U-Net model based on feature selection strategy (FS-U-Net), the proposed scheme achieved the best overall performance, which demonstrated the capability of overcoming the complex cultivation conditions and accurately identifying the fragmented paddy rice fields in Thailand. This study provides a promising tool for large-scale paddy rice monitoring in tropical production regions and has great potential in the global sustainable development of food and environment management.

Paddy field

10.3390/rs13193994

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Citations (48)

Monitoring of large-scale landslides in Zongling, Guizhou, China, with improved distributed scatterer interferometric SAR time series methods

Landslides (2020)

Jing Wang Chao Wang Chou Xie Hong Zhang Yixian Tang

10.1007/s10346-020-01407-5

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Citations (36)