气候变化领域集成服务门户(CCPortal): Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami

CCPortal

DOI	10.1016/j.rse.2020.111743
	Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami
	Moya L.; Muhari A.; Adriano B.; Koshimura S.; Mas E.; Marval-Perez L.R.; Yokoya N.
发表日期	2020
ISSN	00344257
卷号	242
英文摘要	Change detection between images is a procedure used in many applications of remote sensing data. Among these applications, the identification of damaged infrastructures in urban areas due to a large-scale disaster is a task that is crucial for distributing relief, quantifying losses, and rescue purposes. A crucial consideration for change detection is that the images must be co-registered precisely to avoid errors resulting from misalignments. An essential consideration is that some large-magnitude earthquakes produce very complex distortions of the ground surface; therefore, a pair of images recorded before and after a particular earthquake cannot be co-registered accurately. In this study, we intend to identify changes between images that are not co-registered. The proposed procedure is based on the use of phase correlation, which shows different patterns in changed and non-changed areas. A careful study of the properties of phase correlation suggests that it is robust against misalignments between images. However, previous studies showed that, in areas with no-changes, the signal power in the phase correlation is not concentrated in a single component, but rather in several components. Thus, we study the performance of the ℓ1-regularized logistic regression classifier to identify the relevant components of phase correlation and learn to detect non-changed and changes areas. An empirical evaluation consisting of identifying the changes between pre-event and post-event images corresponding to the 2018 Sulawesi Indonesia earthquake-tsunami was performed for this purpose. Pairs of visible and near-infrared (VNIR) spectral bands of medium-resolution were used to compute the phase correlation to set feature space. The phase correlation-based feature space consisted of 484 features. We evaluate the proposed procedure using a damage inventory performed from visual inspection of optical images of 0.5-m resolution. A third-party provided the referred inventory. Because of the limitation of medium-resolution imagery, the different damage levels in the damage inventory were merged into a binary class: “changed” and “non-changed”. The results demonstrate that the proposed procedure efficiently reproduced 85 ± 6% of the damage inventory. Furthermore, our results identified tsunami-affected areas that were not previously identified by visual inspection. © 2020 The Author(s)
英文关键词	Building damage; Phase correlation; Sparse logistic regression; The 2018 Sulawesi Indonesia earthquake-tsunami
语种	英语
scopus关键词	Alignment; Emergency services; Geometrical optics; Infrared devices; Logistic regression; Remote sensing; Tsunamis; Building damage; Damaged infrastructure; Essential considerations; Indonesia; Large scale disasters; Logistic regression classifier; Phase correlation; Visible and near infrared; Earthquakes; correlation; detection method; earthquake event; earthquake magnitude; image resolution; natural disaster; regression analysis; tsunami; Greater Sunda Islands; Sulawesi; Sunda Isles
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179369
作者单位	International Research Institute of Disaster Science, Tohoku University, Aoba 468-1-E301 Aramaki, Aoba-ku, Sendai, 980-8572, Japan; Japan-Peru Center for Earthquake Engineering Research and Disaster Mitigation, National University of Engineering, Tupac Amaru Avenue 1150, Lima, 25, Peru; National Disaster Management Authority of Indonesia, Jakarta, Indonesia; Geoinformatics Unit, RIKEN Center for Advance Intelligence Project, Tokyo, 103-0027, Japan; Graduate School of Information Science, Tohoku University, 6-6-05 Aramaki Aza Aoba, Aoba-ku, Sendai, 980-8579, Japan
推荐引用方式 GB/T 7714	Moya L.,Muhari A.,Adriano B.,et al. Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami[J],2020,242.
APA	Moya L..,Muhari A..,Adriano B..,Koshimura S..,Mas E..,...&Yokoya N..(2020).Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami.Remote Sensing of Environment,242.
MLA	Moya L.,et al."Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami".Remote Sensing of Environment 242(2020).