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

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Rubing Liang , Keren Dai , Juan M. Lopez-Sanchez , Yakun Han , Xianlin Shi , Qiang Xu
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引用次数: 0

摘要

准确和及时地绘制山体滑坡的地图,对于有效的救援行动和全面的灾害评估至关重要。虽然光学图像经常被云和雾遮挡,但合成孔径雷达(SAR)可以独立于天气条件识别山体滑坡。在这项研究中,我们提出了一种深度学习方法,该方法利用形态学模型(DLM)来实现仅使用事件后单时间偏振SAR图像进行准确的滑坡识别。深入分析了不同地物的SAR散射机理和极化特性,选择了最优的极化参数进行深度学习。为了准确地绘制滑坡形状和提取边界,我们引入了多数投票机制和形态优化模型。我们使用一幅四pol ALOS-2图像进行滑坡填图,该方法的总体精度达到95.24%。此外,考虑到四极SAR数据的有限可用性,我们使用双极ALOS-2和Sentinel-1数据来评估该方法在双极数据下的可用性。双pol ALOS-2图像总体精度为89.78%,而Sentinel-1图像有效捕获了滑坡的一般形态,总体精度为76.32%。这表明本文提出的方法在使用单一事件后极化SAR图像进行滑坡制图方面具有很高的适用性,增强了基于SAR的滑坡制图的及时性,提高了应急响应和灾后救援能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
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.
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