An Extensive Review of Deep Learning Driven Remote Sensing Image Classification Models

Abstract

Remote sensing images (RSI) are significant data to examine and observe complete structure on the Earth’s surface. RSI classification has gained significant attention in earth observation technologies, commonly employed in military and civil fields. It becomes a challenging process because of high dimensional features and small amount of labeled data. Advancements in machine learning (ML) and deep learning (DL) models are capable in effective RSI classification. Numerous research is going on in RSI detection and classification area using ML and DL models. In this view, this article focuses on the review of recently developed RSI classification models. A brief introduction to RSI, types, characteristics and challenging issues is given. By a meta-analysis, different approaches related to the RSI classification models are identified and summarized with key findings. Besides, this survey covers the recently developed ML and DL based RSI classification models with their major aim, methodology used, merits, and demerits. At last, a concluding remark related to the present state of art approaches with possible future scope is discussed.