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Many worldwide changing events, including meteorology, weather forecasting, disaster response, and environmental monitoring, are tracked by states or companies via satellite imagery. Early response to disasters critical for human life. In these cases, artificial intelligence applications are also used to make rapid determinations about large geographical region. In this study, satellite images of flooded and undamaged structures in Hurricane Harvey were used. An autoencoder process has been applied to this data set to reduce the noise in satellite imagery. AlexNet, VGG16 deep learning models are used to extract features from both datasets. The most effective features selected by Boruta feature selection algorithm were classified with the Support Vector Machine, and the highest classification accuracy of 99.35% was obtained. Since disasters involve the evaluation of very big data sets from large geographic areas, presenting the data with the smallest possible feature will facilitate the process. For this reason, by applying dimensionality reduction to the selected attributes, a 98.29% success was achieved in the classification with only 90 attributes. The proposed approach shows that deep learning and feature engineering are a very effective method to quickly respond to disaster areas using satellite imagery.
Cite this article as: N. Muzoğlu, E. AdIgüzel, E. Akbacak and M. Kaya Karaslan, “Detection of damaged structures from satellite imagery processed by autoencoder with boruta feature selection method,” Electrica, 23(2), 397-405, 2023.