Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide

Niu, Chaoyang; Zhang, Haobo; Liu, Wei; Li, Runsheng; Hu, Tao

doi:10.1016/j.isprsjprs.2021.01.022

Cited by 24 publications

(12 citation statements)

References 48 publications

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“…For the polarimetric decomposition, the scattering information under different polarization modes can be extracted. It has a strong anti-interference ability, which is conducive to the decomposition and analysis of the scattering mechanism [48]. It provides favorable data support for the identification and detection of building collapse and damage.…”

Section: Polarimetry-based Analysismentioning

confidence: 99%

Using InSAR and PolSAR to Assess Ground Displacement and Building Damage after a Seismic Event: Case Study of the 2021 Baicheng Earthquake

et al. 2022

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During unexpected earthquake catastrophes, timely identification of damaged areas is critical for disaster management. On the 24 March 2021, Baicheng county was afflicted by a Mw 5.3 earthquake. The disaster resulted in three deaths and many human injuries. As an active remote sensing technology independent of light and weather, the increasingly accessible Synthetic Aperture Radar (SAR) is an attractive data for assessing building damage. This paper aims to use Sentinel-1A radar images to rapidly assess seismic damage in the early phases after the disaster. A simple and robust method is used to complete the task of surface displacement analysis and building disaster monitoring. In order to obtain the coseismic deformation field, differential interferometry, filtering and phase unwrapping are performed on images before and after the earthquake. In order to detect the damage area of buildings, the Interferometric Synthetic Aperture Radar (InSAR) and Polarimetric Synthetic Aperture Radar (PolSAR) techniques are used. A simple and fast method combining coherent change detection and polarimetric decomposition is proposed, and the complete workflow is introduced in detail. In our experiment, we compare the detection results with the ground survey data using an unmanned aerial vehicle (UAV) after the earthquake to verify the performance of the proposed method. The results indicate that the experiment can accurately obtain the coseismic deformation field and identify the damaged and undamaged areas of the buildings. The correct identification accuracy of collapsed and severely damaged areas is 86%, and that of slightly damaged and undamaged areas is 84%. Therefore, the proposed method is extremely effective in monitoring seismic-affected areas and immediately assessing post-earthquake building damage. It provides a considerable prospect for the application of SAR technology.

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Section: Polarimetry-based Analysismentioning

confidence: 99%

Using InSAR and PolSAR to Assess Ground Displacement and Building Damage after a Seismic Event: Case Study of the 2021 Baicheng Earthquake

et al. 2022

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“…Landslides seriously threaten the safety of human beings and major infrastructures in mountainous areas (Lei et al, 2019; Niu et al, 2021; Shi et al, 2015; Zhu et al, 2022). Landslide susceptibility evaluation (LSE), which accurately determines the susceptibility of areas to landslides, is the key foundation for prevention and mitigation (Abbaszadeh Shahri et al, 2019; Cantarino et al, 2019; Kadavi et al, 2018; Reichenbach et al, 2018; Xiao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Geo‐environment‐aware adversarial transfer learning method for landslide susceptibility evaluation of complex mountainous areas

Zhang

Zeng

Ding

et al. 2023

Transactions in GIS

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Landslide susceptibility evaluation (LSE) is a critical issue for disaster prevention. Limited by labor cost and observation technology, landslide samples are extremely limited in dense vegetation‐covered and remote areas, making the common supervised learning model underfit with limited samples. Therefore, the reliability of analysis results in mountainous areas is low. Transfer learning can achieve reliable assessment without the need for representative samples. However, transfer learning suffers from environmental heterogeneity in regional LSE and may transfer incorrect classification knowledge of landslide features from dissimilar environments. Aiming at these challenges, we proposed a geo‐environment‐aware LSE method based on unsupervised adversarial transfer learning. The key is to consider the difference in landslide features in different geo‐environments. The study areas were first divided into multiple sub‐environments, and the similarity between the sub‐environments was calculated. Then an environment‐aware adversarial transfer model was built for fine‐grained aligning of the landslide feature with similar sub‐environments and for reducing negative transfer between dissimilar environments. The fitted classification model was employed to predict the target regions and to generate the final LSE. The experimental results indicated that the proposed method achieves reliable LSE for sample‐free regions. The accuracy of the proposed method is 7–12% better than commonly used methods such as support vector machines, random forests, and artificial neural networks. The performance of the proposed method is even close to the results of supervised learning with the presence of representative samples, and it also performs more globally and objectively in susceptibility mapping. These results reveal that the proposed method effectively transfers the knowledge of landslide susceptibility from other regions to the sample‐free region.

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“…Currently, influenced by global climate change, seismic activity and accelerated urbanisation, the number of landslides has gradually increased, and the actual hazards and potential risks have become more prominent. Therefore, timely detection of landslides and determination of their area, scale and distribution are crucial for disaster management and prevention of secondary disasters [2].…”

Section: Introductionmentioning

confidence: 99%

Reg-SA–UNet++: A Lightweight Landslide Detection Network Based on Single-Temporal Images Captured Postlandslide

Niu

Gao

et al. 2022

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

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Landslide detection based on remote sensing images is an effective method for rapidly and accurately detecting landslide regions, which can aid in disaster prevention and mitigation. Landslide detection methods based on semantic segmentation can be used to delineate the scope of landslides while detecting their location. Most existing models use multi-temporal or geological data to improve accuracy. However, the large amount of data introduces additional parameters, consuming significant computing resources. Therefore, this study proposes Reg-SA-UNet++, a model for landslide detection, which uses a single-temporal image captured post-landslide. Reg-SA-UNet++ is based on UNet++ with the following modifications: deep supervised pruning is removed for fewer model parameters and increased detection accuracy; RegNet is employed to replace the convolutional blocks in the encoding process to reduce the number of parameters and improve feature acquisition and attention modules are added at the connection of the convolutional blocks of each layer to strengthen the model's attention to landslide features. The overall accuracy and F1 score of the Reg-SA-UNet++ model for the constructed landslide dataset (93.37% and 92.41%, respectively) and landslide mapping (97.09% and 96.10%, respectively) verify the effectiveness of the proposed model in detecting landslides from remote sensing images.

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Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide

Cited by 24 publications

References 48 publications

Using InSAR and PolSAR to Assess Ground Displacement and Building Damage after a Seismic Event: Case Study of the 2021 Baicheng Earthquake

Using InSAR and PolSAR to Assess Ground Displacement and Building Damage after a Seismic Event: Case Study of the 2021 Baicheng Earthquake

Geo‐environment‐aware adversarial transfer learning method for landslide susceptibility evaluation of complex mountainous areas

Reg-SA–UNet++: A Lightweight Landslide Detection Network Based on Single-Temporal Images Captured Postlandslide

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