Effectiveness of data augmentation on deep based post-hurricane building classification using satellite images

Abstract

While targeting at the classification of images of un-/damaged post-hurricane buildings, this paper investigates into the effects of different data augmentation techniques on three popular convolution neural network models, together with a model built by our team. The main derived results are the test accuracies for each combination of data augmentations and models, showing the competency of a certain combination on our task; thus, comparisons and evaluations are made. The conclusion drawn is that, firstly, convolution neural network models are capable of completing the classification with a rather high rate of success. And it is shown that there could possibly exist such data augmentation techniques that do not improve the performance of a model.