Hurricane damage assessment in satellite images using hybrid VGG16 model

Hurricanes are one of the most disastrous natural phenomena occurring on Earth that cause loss of human lives and immense damage to property. A damage assessment method has been proposed for damage caused to buildings due to Hurricane Harvey that hit the Texas region in the year 2017. The aim of our study is to predict if there is any damage to the buildings present in the postdisaster satellite images. Principal component analysis has been used for the visualization of data. The VGG16 model has been used for extracting features from the input images. K-nearest neighbor (KNN), logistic regression, decision tree, random forest, and XGBoost classification techniques have been used for classification of the images whose features have been extracted from VGG16. Best accuracy of 97% is obtained by KNN classifier for the balanced test set, and accuracy of 96% is obtained by logistic regression for the unbalanced test set.