Publication Type : Book Chapter
Publisher : Springer Verlag
Source : Studies in Computational Intelligence, Springer Verlag, Volume 804, p.401-424, Springer, Cham (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058989573&doi=10.1007%2f978-3-030-03000-1_16&partnerID=40&md5=020c0e81b9131569f4702dc4e41b699e
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2019
Abstract : Hyperspectral remote sensing has received considerable interest in recent years for a variety of industrial applications including urban mapping, precision agriculture, environmental monitoring, and military surveillance as well as computer vision applications. It can capture hyperspectral image (HSI) with a lager number of land-cover information. With the increasing industrial demand in using HSI, there is a must for more efficient and effective methods and data analysis techniques that can deal with the vast data volume of hyperspectral imagery. The main goal of this chapter is to provide the overview of fundamentals and advances in hyperspectral images. The hyperspectral image enhancement, denoising and restoration, classical classification techniques and the most recently popular classification algorithm are discussed with more details. Besides, the standard hyperspectral datasets used for the research purposes are covered in this chapter. © Springer Nature Switzerland AG 2019.
Cite this Research Publication : Sowmya V., Dr. Soman K. P., and Hassaballah, M., “Hyperspectral image: Fundamentals and advances”, in Studies in Computational Intelligence, vol. 804, Springer Verlag, 2019, pp. 401-424, Springer, Cham.