Rubing Liang , Keren Dai , Juan M. Lopez-Sanchez , Yakun Han , Xianlin Shi , Qiang Xu
{"title":"利用形态学模型的深度学习方法从事件后单时间极化SAR图像中绘制滑坡图","authors":"Rubing Liang , Keren Dai , Juan M. Lopez-Sanchez , Yakun Han , Xianlin Shi , Qiang Xu","doi":"10.1016/j.rse.2025.114904","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate and timely mapping of landslides after the event (e.g., earthquake) is crucial for effective rescue operations and comprehensive disaster assessment. While optical images are often obstructed by clouds and fog, synthetic aperture radar (SAR) can identify landslides independently of weather conditions. In this study, we propose a deep learning method which exploits a morphological model (DLM) to achieve accurate landslide identification using only a post-event single-temporal polarimetric SAR image. The SAR scattering mechanisms and polarimetric characteristics of various ground objects are thoroughly analyzed to select optimal polarimetric parameters for deep learning. To accurately map landslide shapes and extract boundaries, we introduce a Majority Voting mechanism and a morphological optimization model. We have used one quad-pol ALOS-2 image for landslide mapping and achieved an overall accuracy of 95.24 % with the proposed method. Additionally, considering the limited availability of quad-pol SAR data, we have employed dual-pol ALOS-2 and Sentinel-1 data to assess the method's usability with dual-pol data. The dual-pol ALOS-2 image achieved an overall accuracy of 89.78 %, while Sentinel-1 image effectively captured the general landslide shape with an overall accuracy of 76.32 %. This demonstrates the high applicability of the proposed method for landslide mapping using a single post-event polarimetric SAR image, enhancing the timeliness of SAR-based landslide mapping and improving emergency response and post-disaster rescue capabilities.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114904"},"PeriodicalIF":11.1000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Landslide mapping from post-event single-temporal polarimetric SAR image by a deep learning method exploiting a morphological model\",\"authors\":\"Rubing Liang , Keren Dai , Juan M. Lopez-Sanchez , Yakun Han , Xianlin Shi , Qiang Xu\",\"doi\":\"10.1016/j.rse.2025.114904\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate and timely mapping of landslides after the event (e.g., earthquake) is crucial for effective rescue operations and comprehensive disaster assessment. While optical images are often obstructed by clouds and fog, synthetic aperture radar (SAR) can identify landslides independently of weather conditions. In this study, we propose a deep learning method which exploits a morphological model (DLM) to achieve accurate landslide identification using only a post-event single-temporal polarimetric SAR image. The SAR scattering mechanisms and polarimetric characteristics of various ground objects are thoroughly analyzed to select optimal polarimetric parameters for deep learning. To accurately map landslide shapes and extract boundaries, we introduce a Majority Voting mechanism and a morphological optimization model. We have used one quad-pol ALOS-2 image for landslide mapping and achieved an overall accuracy of 95.24 % with the proposed method. Additionally, considering the limited availability of quad-pol SAR data, we have employed dual-pol ALOS-2 and Sentinel-1 data to assess the method's usability with dual-pol data. The dual-pol ALOS-2 image achieved an overall accuracy of 89.78 %, while Sentinel-1 image effectively captured the general landslide shape with an overall accuracy of 76.32 %. This demonstrates the high applicability of the proposed method for landslide mapping using a single post-event polarimetric SAR image, enhancing the timeliness of SAR-based landslide mapping and improving emergency response and post-disaster rescue capabilities.</div></div>\",\"PeriodicalId\":417,\"journal\":{\"name\":\"Remote Sensing of Environment\",\"volume\":\"328 \",\"pages\":\"Article 114904\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2025-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing of Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0034425725003086\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425725003086","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Landslide mapping from post-event single-temporal polarimetric SAR image by a deep learning method exploiting a morphological model
Accurate and timely mapping of landslides after the event (e.g., earthquake) is crucial for effective rescue operations and comprehensive disaster assessment. While optical images are often obstructed by clouds and fog, synthetic aperture radar (SAR) can identify landslides independently of weather conditions. In this study, we propose a deep learning method which exploits a morphological model (DLM) to achieve accurate landslide identification using only a post-event single-temporal polarimetric SAR image. The SAR scattering mechanisms and polarimetric characteristics of various ground objects are thoroughly analyzed to select optimal polarimetric parameters for deep learning. To accurately map landslide shapes and extract boundaries, we introduce a Majority Voting mechanism and a morphological optimization model. We have used one quad-pol ALOS-2 image for landslide mapping and achieved an overall accuracy of 95.24 % with the proposed method. Additionally, considering the limited availability of quad-pol SAR data, we have employed dual-pol ALOS-2 and Sentinel-1 data to assess the method's usability with dual-pol data. The dual-pol ALOS-2 image achieved an overall accuracy of 89.78 %, while Sentinel-1 image effectively captured the general landslide shape with an overall accuracy of 76.32 %. This demonstrates the high applicability of the proposed method for landslide mapping using a single post-event polarimetric SAR image, enhancing the timeliness of SAR-based landslide mapping and improving emergency response and post-disaster rescue capabilities.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.