Abstract

With the recent tremendous improvements in the spatial, spectral, and temporal resolutions of remote sensing imaging systems, there has been a dramatic increase in their applications. Amongst different applications of very high-resolution remote sensing images, damage detection for rapid emergency response is one of the most challenging ones. Recently, deep learning frameworks have enhanced the performance of earthquake damage detection by automatic extraction of strong deep features. However, most of the existing studies in this area focus on using nadir satellite images or orthophotos which limits the available data sources. The objective of this study is to present a multi-modal integrated structure to combine orthophoto and off-nadir images for earthquake building damage detection. In this context, a multi-feature fusion method based on deep transfer learning is presented, which contains four different steps, namely pre-processing, deep feature extraction, deep feature fusion, and transfer learning. To validate the presented framework, two comparative experiments are conducted on the 2010 Haiti earthquake, using pre- and post-event off-nadir satellite images, which were collected by WorldView-2 (WV-2) satellite platform as well as a post-event airborne orthophoto. The results demonstrate considerable advantages in identifying damaged and non-damaged buildings with over 83% for the overall accuracy.

摘要

随着最近遥感成像系统空间、光谱和时间分辨率的巨大改进，它们的应用急剧增加。在超高分辨率遥感图像的不同应用中，快速应急响应的损坏检测是最具挑战性的应用之一。最近，深度学习框架通过自动提取强大的深度特征来增强地震损伤检测的性能。然而，该领域现有的大多数研究都集中在使用最低点卫星图像或正射影像，这限制了可用的数据来源。本研究的目的是提出一种多模态集成结构，将正射影像和非天底图像结合起来进行地震建筑物损坏检测。在这种情况下，提出了一种基于深度迁移学习的多特征融合方法，包括预处理、深度特征提取、深度特征融合和迁移学习四个不同的步骤。为了验证所提出的框架，我们对 2010 年海地地震进行了两项对比实验，使用的是 WorldView-2 (WV-2) 卫星平台以及后事件机载正射影像。结果表明，在识别损坏和未损坏建筑物方面具有相当大的优势，整体准确度超过 83%。使用 WorldView-2 (WV-2) 卫星平台以及事后机载正射影像收集的事前和事后离天底卫星图像对 2010 年海地地震进行了两次对比实验。结果表明，在识别损坏和未损坏建筑物方面具有相当大的优势，整体准确度超过 83%。使用 WorldView-2 (WV-2) 卫星平台以及事后机载正射影像收集的事前和事后离天底卫星图像对 2010 年海地地震进行了两次对比实验。结果表明，在识别损坏和未损坏建筑物方面具有相当大的优势，整体准确度超过 83%。