Transfer Learning Based Convolutional Neural Network for Classification of Remote Sensing Images

of Land cover Remote sensing images find a lot of applications including regional planning, natural resources conservation and management, agricultural monitoring etc., Presently, Convolutional Neural Networks (CNN) which are deep learning based methods are successfully employed for classification problems due to its flexible architecture and potentiality to learn new features from raw data. The motivation of the work is to implement a robust deep learning architecture for the classification of remote sensing images using a transfer learning approach. Deep learning requires a large amount of time if the training is initiated from scratch. Transfer learning overcomes this drawback by using pre-trained models efficiently. In the proposed work, a transfer learning based Convolutional Neural Network is used for the classification of remote sensing images. Three popular pre-trained models - VGG16, ResNet50 and Densenet121 are used for feature extraction and a fully connected layer is used for classification. Results indicate that the transfer learning based Convolutional Neural Network with data augmentation and optimization of model parameters gives better performance compared to training from scratch for the classification of remote sensing images. Experimental results indicate that an improved accuracy of 95.88% is obtained for the proposed Transfer learning method for the remote sensing dataset of UC-Merced.