Paper Contents
Abstract
Remote sensing has become a crucial tool for studying the earth's natural resources and environment. However, handling the vast amounts of data generated by remote sensing sensors is a significant challenge. Lossy image compression has emerged as a solution by producing compressed images with some data loss while maintaining image quality. This paper explores the different techniques and algorithms used for lossy image compression in the remote sensing domain, with a focus on multichannel remote sensing for quality control of images. The paper also discusses various image classifications and their applications, such as mapping crops and forest areas. The research aims to provide insights into the potential of lossy compression for remote sensing applications. The paper also presents a literature survey of studies that have investigated the effect of lossy compression on the classification of remote sensing imagery, with recommendations for further research.
Copyright
Copyright © 2024 Sanketh Kumar. This is an open access article distributed under the Creative Commons Attribution License.
