Intelligent Remote Sensing: Applications and Techniques

Abstract

The development of remote sensing technologies over the last several decades has resulted in considerable improvements, which have made it possible for us to gather data of a high quality about the surface of the Earth and the characteristics it has. Yet, in terms of data processing and interpretation, the enormous volumes of data that are created by remote sensing devices provide a tremendous problem. The purpose of this paper is to provide a review of the most recent and cutting-edge techniques and applications of intelligent remote sensing. These techniques and applications aim to address these challenges by integrating machine learning, artificial intelligence, and other advanced computing techniques into the processing and analysis of remote sensing data. In this article, we will go through the fundamental components of intelligent remote sensing. These components include data collecting, data preprocessing, feature extraction, classification, and prediction. We also emphasize the potential of intelligent remote sensing in the management of real-world issues such as environmental monitoring, mapping land use and land cover, disaster management, and the mitigation of climate change.