Change detection analysis of tornado disaster using conditional copulas and Data Fusion for cost-effective disaster management

The up-to-date results are presented from an ongoing study of the Data Fusion of multi-temporal and multi-sensor satellite datasets for near real time damage and debris assessment after a tornado disaster event. The space-borne sensor datasets comprising of: (i) C-band SAR dataset from RADARSAT-2; (ii) Multi-Spectral (MS) optical dataset including NIR from RapidEye; (iii) MS and panchromatic dataset of Advanced Linear Imaging (ALI), are studied for multi-sensor data fusion. A combined approach of multi-polarized radiometric and textural feature extraction, and statistical learning based feature classification is devised for fine tuning of the complex and generalized change detection model. We also investigated the use of multi-variate conditional copula as a classifier technique, by formulating the change and no-change as a binary-class classification problem in this study. The classification results from the above technique are used for assessment of damage and debris cover after the tornado disaster event. The performance of the above approach yields a very significant Kappa accuracy up to 75%. A 10-fold cross validation strategy is used for quantitative analysis of the performance of the classification model. This study will be further extended for modelling the effect of incidence angle discrepancies or climatic condition variances, which will address the heterogeneity factor in terms of local statistics of the dataset.