Contextual classification using photometry and elevation data for damage detection after an earthquake event. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Rupnik, Ewelina; Nex, Francesco; Toschi, Isabella; Remondino, Fabio
• Is Part Of: European journal of remote sensing. Volume 51:Issue 1(2018)
• Page Start: 543
• Page End: 557
• Publication Date: 2018-01-01
• Subjects: Digital Surface Model -- orthophoto -- classification -- supervised -- unsupervised -- damage assessment; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2018.1458584
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: This research presents a processing workflow to automatically find damaged building areas in an urban context. The input data requirements are high-resolution multi-view images, acquired from airborne platform. The elevations are derived from a dense surface model generated with photogrammetric methods. With the principal objective of rapid response in emergency situations, two different processing roadmaps are proposed, semi-supervised and unsupervised. Both of them follow a two-step workflow of building detection and building health estimation. Optionally, cadastral layers may serve as a-priori knowledge on building location. The semi-supervised approach involves a data training step, while the unsupervised approach exploits the similarities and dissimilarities between sets of features calculated over the detected buildings. The change detection task is formulated as a classification task defined over a conditional random field. The algorithms are evaluated using two datasets (Vexcel and Midas cameras) and results are compared with ground truth data and specific metrics.