This article presents an effective classification method for earthquake damage mapping from unmanned aerial vehicles (UAV) photogrammetric point clouds. The classification method consists of three main components: (a) construction of a point feature descriptor regarding to spectral,
textural, and geometrical features, (b) optimization of collecting informative training samples through an active learning (AL) method, and (c) fine-tuning the point-based classification results with contextual information. Besides using existing spectral and geometrical features, we design
a textural feature based on fractal theory to construct a point feature descriptor through linear combination. A batch-model AL method called Margin Sampling and Multiclass Level Uncertainty (MS-MCLU) is proposed based on classification uncertainty using a Support Vector Machine classifier.
We use a multi-label Markov random fields to fine-tune the point-based classification results with a pairwise model. The proposed method was tested using three sets of point clouds generated from UAV images over Mirabello, Lushan, and Wenchuan earthquake scenarios in 2012, Italy, and in 2013
and 2008, China, respectively. The proposed classification method was compared with that of two other feature descriptors, i.e. spectral combined with textural features (Spe_Tex) and geometrical features (Geo). The results show that classification accuracies were improved by using the proposed
point feature descriptor. Results also show that the proposed MS-MCLU AL method evidently saved the cost of collecting informative training samples and produced higher classification accuracies than a random sampling strategy. Moreover, contextual information contributed to the improvement
on the point-based classification results and was suggested to be considered in earthquake damage mapping applications.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing, China
School of Geosciences and Info-physics, Central South University, Changsha, China
Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
Publication date: August 18, 2018
More about this publication?