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Abstract

With the continuous development of synthetic aperture radar technology, the application of SAR image data in landslide hazard information extraction has received attention and achieved some results. In this paper, the grayscale texture information features of SAR images after landslide disasters are combined with the expectation maximization algorithm to extract landslide information. The optimal threshold is calculated using the expectation maximization algorithm, and the image is divided into a landslide area and a non-landslide area.In this paper, the method is applied to Radarsat-2 SAR data in Yingxiu area after Wenchuan earthquake to obtain the distribution of landslide results. The method is also applied to other areas of the image for landslide extraction, and the correctness of the results is judged by comparing with the field measurement data and optical image analysis. Due to the particularity of the landslide, the method still needs further improvement in accuracy. The extraction results need further research in verification, so the method is not very good at present. Improvement needs further improvement.

Keywords

SAR image landslide hazard disaster information extraction

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References

Lin

Wang

Yang

et al., Landslide identification and information extraction based on optical and multispectral uav remote sensing imagery, IOP Conference Series: Earth and Environmental Science57 (2017), 012017.

Cai

Wang

Mao

et al., An adaptive offset tracking method with sar images for landslide displacement monitoring, Remote Sensing9(8) (2017), 830.

Singh

and Kumar

, Hazard assessment of landslide disaster using information value method and analytical hierarchy process in highly tectonic chamba region in bosom of himalaya, Journal of Mountain Science15(4) (2018), 808–824.

Hong

Wang

et al., A landslide intelligent detection method based on CNN and RSG_R, IEEE International Conference on Mechatronics & Automation, 2017.

Liu

Shi

Wang

and Wu

, Seismic landslide hazard assessment in the tianshui area, china, based on scenario earthquakes, Bulletin of Engineering Geology & the Environment (2017), 1–10.

Pradhan

A.M.S.

Kang

H.S.

Lee

J.S.

et al., An ensemble landslide hazard model incorporating rainfall threshold for mt. umyeon, South Korea, Bulletin of Engineering Geology & the Environment (1) (2017), 1–16.

Chimidi

Raghuvanshi

T.K.

and Suryabhagavan

K.V.

, Landslide hazard evaluation and zonation in and around gimbi town, western ethiopia – a gis-based statistical approach, Applied Geomatics (1–4) (2017), 1–18.

Zhou

Wang

et al., Polarimetric sar image classification using deep convolutional neural networks, IEEE Geoscience & Remote Sensing Letters13(12) (2017), 1935–1939.

Ambrosi

Strozzi

Scapozza

et al., Landslide hazard assessment in the himalayas (nepal and bhutan) based on earth-observation data, Engineering Geology (2018), 237.

10.

Ming

et al., Robust optical-to-sar image matching based on shape properties, IEEE Geoscience & Remote Sensing Letters14(4) (2017), 564–568.

Research on landslide hazard information extraction method based on SAR image

Abstract

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References