Deep Learning for Satellite Image Classification

Nowadays, large amounts of high resolution remote-sensing images are acquired daily. However, the satellite image classification is requested for many applications such as modern city planning, agriculture and environmental monitoring. Many researchers introduce and discuss this domain but still, the sufficient and optimum degree has not been reached yet. Hence, this article focuses on evaluating the available and public remote-sensing datasets and common different techniques used for satellite image classification. The existing remote-sensing classification methods are categorized into four main categories according to the features they use: manually feature-based methods, unsupervised feature learning methods, supervised feature learning methods, and object-based methods. In recent years, there has been an extensive popularity of supervised deep learning methods in various remote-sensing applications, such as geospatial object detection and land use scene classification. Thus, the experiments, in this article, carried out on one of the popular deep learning models, Convolution Neural Networks (CNNs), precisely AlexNet architecture on a standard sounded dataset, UC-Merceed Land Use. Finally, a comparison with other different techniques is introduced.