Rapid detection of damaged buildings after natural disasters, such as earthquakes and hurricanes, is an urgent need for first response, rescue and recovery planning. In this context, post-event aerial images which could be collected right after disasters are valuable sources for damage detection. However, manual analysis process of the acquired imagery could be both time consuming and costly. To address this issue, a series of classification models for post-hurricane automated detection of damaged buildings is presented in this paper. First, five feature sets were generated through feature extraction and transformation. Then, several classifiers were trained using two groups of classification methods: (1) the Minimum-distance and (2) the Support Vector Machine (SVM) methods. The effectiveness of these classifiers was evaluated in terms of classification accuracies and testing time. The results demonstrated the combination of feature sets and classification methods can provide the best performance. Furthermore, optimal classifiers were selected for future automated real-time damaged building detection. The observed performances of these optimal classifiers indicate promising application for a wide variety of image-based classification tasks.
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