Empirical evaluation of dissimilarity measures for use in urban structural damage detection

Multi-temporal earth-observation imagery is now available at sub-meter accuracy and has been found very useful for performing quick damage detection for urban areas affected by large-scale disasters. The detection of structural damage using images taken before and after disaster events is usually modeled as a change detection problem. In this paper, we propose a new perspective for performing change detection, where dissimilarity measures are used to extract urban structural damage. First, image gradient magnitudes and spatial variances are used as a means to capture urban structural features. Subsequently, a family of distribution dissimilarity measures, including: Euclidean distance, Cosine, Jeffery divergence, and Bhattacharyya distance, are used to extract structural damage. We particularly focus on evaluating the performance of these dissimilarity-based change detection methods under the framework of pattern classification and cross-validation, and with the use of a pair of bi-temporal satellite images captured before and after a major earthquake in Barn, Iran. The paper concludes that the proposed change detection methods for urban structural damage detection, which are conceptually simple and computationally efficient, outperform the traditional correlation analysis in terms of both classification accuracy and tolerance to local alignment errors.