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Detecting urban changes using phase correlation and l(1)-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami

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dc.contributor.author Moya, Luis es_PE
dc.contributor.author Muhari, Abdul es_PE
dc.contributor.author Adriano, Bruno es_PE
dc.contributor.author Koshimura, Shunichi es_PE
dc.contributor.author Mas, Erick es_PE
dc.contributor.author Marval-Perez, Luis R. es_PE
dc.contributor.author Yokoya, Naoto es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2020
dc.description.abstract 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.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1016/j.rse.2020.111743
dc.identifier.uri https://hdl.handle.net/20.500.12390/2812
dc.language.iso eng
dc.publisher Elsevier BV
dc.relation.ispartof REMOTE SENSING OF ENVIRONMENT
dc.rights info:eu-repo/semantics/openAccess
dc.subject Soil Science
dc.subject Computers in Earth Sciences es_PE
dc.subject Geology es_PE
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.08
dc.title Detecting urban changes using phase correlation and l(1)-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
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