Using classic networks for classifying remote sensing images: Comparative study

This paper presents a comparative study for using the deep classic convolution networks in remote sensing images classification. There are four deep convolution models that used in this comparative study; the DenseNet 196, the NASNet Mobile, the VGG 16, and the ResNet 50 models. These learning convolution models are based on the use of the ImageNet pre-trained weights, transfer learning, and then adding a full connected layer that compatible with the used dataset classes. There are two datasets are used in this comparison; the UC Merced land use dataset and the SIRI-WHU dataset. This comparison is based on the inspection of the learning curves to determine how well the training model is and calculating the overall accuracy that determines the model performance. This comparison illustrates that the use of the ResNet 50 model has the highest overall accuracy and the use of the NASNet Mobile model has the lowest overall accuracy in this study. The DenseNet 169 model has little higher overall accuracy than the VGG 16 model. © 2020 ASTES Publishers. All rights reserved.