A Comparative Study of Deep Learning and Traditional Methods for Environmental Remote Sensing

Abstract

Because of the accessibility of massive data from remote sensing data and developments in ML, machine learning (ML) techniques have been extensively applied in environmental remote sensing research. Modern machine learning (ML) frameworks like deep learning (DL) have significantly outperformed older models in terms of performance. This study focuses on the software that uses a traditional neural network (NN) as well as Deep Learning (DL) approaches in environmental remote sensing, which also covers land cover mapping, retrieval of environmental parameters, data fusion, image compression, and information reconstruction and prediction. It is also explained how DL may be used to monitor other aspects of the ecosystem, including the environment, water management, ground and air surface temperatures, transpiration, ultraviolet (UV) rays, and sea color all factors to consider. Following that, the essay explores the challenges and prospective uses of DL in environmental remote sensing.