Satellite image classification using deep learning approach

Our planet Earth comprises distinguished topologies based on temperature, location, latitude, longitude, and altitude, which can be captured using Remote Sensing Satellites. In this paper, the classification of satellite images is performed based on their topologies and geographical features. Researchers have worked on several machine learning and deep learning methods like support vector machine, k-nearest neighbor, maximum likelihood, deep belief network, etc. that can be used to solve satellite image classification tasks. All strategies give promising results. Recent trends show that a Convolutional Neural Network (CNN) is an excellent deep learning model for classification purposes, which is used in this paper. The open-source EuroSAT dataset is used for classifying the remote images which contain 27,000 images distributed among ten classes. The 3 baseline CNN models are pre-trained, namely- ResNet50, ResNet101, and GoogleNet models. They have other sequence layers added to them with respect to CNN, and data is pre-processed using LAB channel operations. The highest accuracy of 99.68%, precision of 99.42%, recall of 99.51%, and F- Score of 99.45% are achieved using GoogleNet over the pre-processed dataset. The proposed work is compared with the state-of-art methods and it is observed that more layers in CNN do not necessarily provide a better outcome for a medium-sized dataset. The GoogleNet, a 22-layer CNN, performs faster and better than the 50 layers CNN- ResNet50, and 101 layers CNN- ResNet101.