Earthquake Damage Detection in Urban Areas using Curvilinear Features
Brett, PTB and Guida, R (2013) Earthquake Damage Detection in Urban Areas using Curvilinear Features IEEE Transactions on Geoscience and Remote Sensing.
Available under License : See the attached licence file.
Bright curvilinear features arising from the geometry of man-made structures are characteristic of synthetic aperture radar (SAR) images of urban areas, particularly due to double-reflection mechanisms. An approach to urban earthquake damage detection using double-reflection line amplitude change in single-look images has been established in previous literature. Based on this method, this paper introduces an automated tool for fast, unsupervised damage detection in urban areas. Ridge-based curvilinear features are extracted from a preevent SAR image, and double-reflection candidates are selected using prior probability distributions derived from a simple geometrical building model. The candidate features are then used with the ratio of a pair of single preevent and postevent SAR single-look amplitude images to estimate damage levels. The algorithm is very efficient, with overall computational complexity of $O(Nlog k)$ for an $N$-pixel image containing features of mean length $k$. The technique is demonstrated using COSMO-SkyMed data covering L'Aquila, Italy, and Port-au-Prince, Haiti.
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering|
|Date :||1 August 2013|
|Identification Number :||10.1109/TGRS.2013.2271564|
|Additional Information :||© YEAR 2013. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the author's version accepted for publication in IEEE Transactions on Geoscience and Remote Sensing, 2013 http://dx.doi.org/10.1109/TGRS.2013.2271564|
|Depositing User :||Symplectic Elements|
|Date Deposited :||28 Aug 2013 10:59|
|Last Modified :||09 Jun 2014 13:37|
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