A Review of Remote Sensing Applications on Very High-Resolution Imagery Using Deep Learning-Based Semantic Segmentation Techniques

Abstract

Semantic Segmentation is a technique in Computer Sciences (CS) to extract information from images. Recent advances in Artificial Intelligence, particularly in Deep Learning, semantic segmentation combined with techniques such as convolutional neural networks have presented better results and exciting results. Due to its power and better results than classical approaches, there has been an increase of research articles in Remote Sensing that propose the use of deep learning based semantic segmentation to extract information from satellite or airborne imagery. In this paper we surveyed the state-of-the- art of Semantic Segmentation in Remote Sensing, from 2010 until 2020 by identifying the research topics, the number of publications and citations, the key researchers and the leading countries. Furthermore, we also pointed out the key algorithms, the main convolutional neural network architectures, backbones and the most used evaluation metrics. In addition, some datasets were highlighted, as well as some frameworks that can be used to train semantic segmentation deep neural networks. Finally, we shown some applications of the showcased techniques and provide a conclusion with the research opportunities of Remote Sensing Semantic Segmentation, with respect to some bleeding-edge scientific papers published in 2020 in CS.