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

卷:242

Article

Moya, Luis^1,2  Muhari, Abdul³  Adriano, Bruno⁴  Koshimura, Shunichi¹  Mas, Erick¹  Marval-Perez, Luis R.⁵  Yokoya, Naoto⁴ 

[1] Tohoku Univ, Int Res Inst Disaster Sci, Aoba Ku, Aoba 468-1-E301, Sendai, Miyagi 9808572, Japan

[2] Natl Univ Engn, Japan Peru Ctr Earthquake Engn Res & Disaster Mit, Tupac Amaru Ave 1150, Lima 25, Peru

[3] Natl Disaster Management Author Indonesia, Jakarta, Indonesia

[4] RIKEN, Geoinformat Unit, Ctr Adv Intelligence Project, Tokyo 1030027, Japan

[5] Tohoku Univ, Grad Sch Informat Sci, Aoba Ku, 6-6-05 Aramaki Aza Aoba, Sendai, Miyagi 9808579, Japan

关键词: Building damage;    Phase correlation;    Sparse logistic regression;    The 2018 Sulawesi Indonesia earthquake-tsunami;   

DOI  :  10.1016/j.rse.2020.111743

来源: Elsevier

【 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 l(1)-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.

【 授权许可】

Free