Remote sensing image scene classification has a wide scope of applications and hence has been receiving
remarkable attention. Nowadays, Deep learning (DL) algorithms have seen a massive rise in popularity
for remote-sensing image analysis over the past few years. This paper first provides a comprehensive
review of the recent progress in remote sensing applications. 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[15]. This article then focuses on evaluating the available and public remote-sensing
datasets. Finally, a conclusion regarding the current state of art methods and directions for future
research are presented.