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

Changxiong Liu; Ruochen Sun; Shikai Zhuang

doi:10.1117/12.2640917

13 October 2022 Effectiveness of data augmentation on deep based post-hurricane building classification using satellite images

Changxiong Liu, Ruochen Sun, Shikai Zhuang

Author Affiliations +

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228713 (2022) https://doi.org/10.1117/12.2640917
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

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.

Citation Download Citation

Changxiong Liu, Ruochen Sun, and Shikai Zhuang "Effectiveness of data augmentation on deep based post-hurricane building classification using satellite images", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228713 (13 October 2022); https://doi.org/10.1117/12.2640917

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