Post-Disaster image analysis using domain adaptation

There is a need for rapid response during disasters. However, there is a paucity of training data which leads to classification models that do not generalize well. If the pre disaster data is used to augment the training data, the models perform poorly due to statistical distribution differences between pre and post disaster conditions. Also, it is challenging to analyze large areas for identifying the disaster affected regions by visual image interpretation techniques. Using the limited available ground truth, during post disasters, a domain adaptation (DA) approach is used to study the post-earthquake situations. Further, knowledge about the spatial relationships with adjacent regions is integrated with the DA approach to refine the classification. The results were compared to traditional classification methods and were found to achieve higher accuracies with smaller training sample sizes.