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

No hay miniatura disponible
Aguilera-Betti, Isabella
Aliste, Diego
Alvarez, Claudio
Aravena, Juan C.
Barichivich, Jonathan
Bianchi, Lucas O.
Boninsegna, Jose A.
Christie, Duncan A.
Cook, Edward R.
Couvreux, Fleur
Título de la revista
Revista ISSN
Título del volumen
Elsevier BV
Proyectos de investigación
Unidades organizativas
Número de la revista
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.
Palabras clave
Soil Science, Computers in Earth Sciences, Geology