SAR Image Earthquake Landslide Recognition Based on Multi-feature Cross-Fused Siamese Network

CHEN Lifu; JIN Yuchen; LI Zhenhong; SONG Chuang; WANG Xiaohua; CHEN Haoda; LIU Yawu; ZHAO Junqi

doi:10.13203/j.whugis20230479

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SAR Image Earthquake Landslide Recognition Based on Multi-feature Cross-Fused Siamese Network

Photogrammetry and Remote Sensing | 更新时间：2026-11-05

- SAR Image Earthquake Landslide Recognition Based on Multi-feature Cross-Fused Siamese Network
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 5, Pages: 917-927(2025)
- 作者机构：
  
  1.长沙理工大学电气与信息工程学院，湖南长沙，410004
  2.长安大学地质工程与测绘学院，陕西西安，710054
  3.长安大学地学与卫星大数据研究中心，陕西西安，710054
  4.自然资源部生态地质与灾害防控重点实验室，陕西西安，710054
  5.西部矿产资源与地质工程教育部重点实验室，陕西西安，710054
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20230479
  CLC： P237
- Received：26 January 2024，
  
  Published：05 May 2025
- 稿件说明：
移动端阅览
陈立福,金昱忱,李振洪，等.基于多特征交叉融合孪生网络的SAR影像地震滑坡识别[J].武汉大学学报（信息科学版）,2025,50(5):917-927.

CHEN Lifu,JIN Yuchen,LI Zhenhong,et al.SAR Image Earthquake Landslide Recognition Based on Multi-feature Cross-Fused Siamese Network[J].Geomatics and Information Science of Wuhan University,2025,50(5):917-927.
陈立福,金昱忱,李振洪，等.基于多特征交叉融合孪生网络的SAR影像地震滑坡识别[J].武汉大学学报（信息科学版）,2025,50(5):917-927. DOI：10.13203/j.whugis20230479

CHEN Lifu,JIN Yuchen,LI Zhenhong,et al.SAR Image Earthquake Landslide Recognition Based on Multi-feature Cross-Fused Siamese Network[J].Geomatics and Information Science of Wuhan University,2025,50(5):917-927. DOI：10.13203/j.whugis20230479

摘要

由地震引发的群发性大规模地震滑坡是一类非常严重的震后次生灾害，会造成严重的人员伤亡和巨大的经济财产损失。地震发生后迅速准确地识别出这些地震滑坡，可为国家应急部门评估灾害程度和制定救灾措施提供非常重要的信息指导。合成孔径雷达（synthetic aperture radar，SAR）具有全天时、全天候成像特性，但由于地震滑坡背景极为复杂，在SAR影像中特征不显著，因此目前识别效果较差。对此，提出一个使用SAR图像进行滑坡识别的差异特征与聚合特征交叉融合孪生网络。该网络由编码-解码结构组成，编码器采用孪生结构的特征提取网络对地震前后的SAR图像进行不同分辨率的特征提取；解码器中通过构建多尺

度差异特征和聚合特征生成模块，对不同尺度地震滑坡的特征图进行差异提取与特征聚合，以此来充分表征地震滑坡特性。并通过提出的多特征交叉融合模块对不同尺度的聚合特征和差异特征进行密集连接逐层解码，提升细节特征与语义特征的提取性能，最终得到识别结果。利用Sentinel-1数据对巴布亚新几内亚地震和中国西藏米林地震滑坡进行了实验，独立测试实验的滑坡识别精度为：查准率分别为70.75%和76.52%，召回率分别为60.92%和71.20%，

1分数分别为65.46%和74.03%，总体准确率分别为91.00%和86.14%。利用该网络，地震滑坡发生后可利用SAR影像迅速识别滑坡区，进行灾害应急响应。

Abstract

Objectives

Earthquake-induced mass landslides are a severe type of secondary disaster following earthquakes

causing significant casualties and substantial economic losses. Rapidly and accurately identifying these earthquake-induced landslides after an event is crucial for national emergency departments to assess disaster severity and formulate relief measures. Although synthetic aperture radar (SAR) offers all-time

all-weather imaging capabilities

its effectiveness in identifying landslides is currently limited due to the complexity of the background and the less prominent features of landslides in SAR images.

Methods

This paper proposes a siamese network for landslide identification in SAR images

named the difference and aggregated feature cross fusion siamese network (DACS-Net). This network

composed of an encoder-decoder structure

utilizes a siamese feature extraction network in the encoder to extract features of pre- and post-earthquake SAR images at different resolutions. In the decoder

multi-scale difference feature and aggregated feature generation modules are constructed to perform difference extraction and feature aggregation for landslide feature maps at different scales

fully representing the characteristics of earthquake-induced landslides. The proposed multi-feature cross-fusion module densely connects and decodes different scales of aggregated and difference features layer by layer

enhancing the extraction of detail and semantic features

leading to the identification results.

Results

Experiment utilized Sentinel-1 data on earth

quake-triggered landslides in Papua New Guinea and Milin

results show that the proposed method could effectively identify earthquake-induced landslides. The landslide identification precision could reached 70.75% and 76.52%

recall were 60.92% and 71.20%

1 score were 65.46% and 74.03%

and overall accuracy were 91.00% and 86.14%.

Conclusion

By utilizing this network

achievements have been made in the recognition of landslides in SAR images

enhancing the practical application value of deep learning networks in landslide detection. This holds significant importance for disaster emergency response.

关键词

Keywords

references

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