Classification of Hyperspectral Remote Sensing Images for Crop Type Identification: State of the Art

Kawtar El Karfi, Sanaa El Fkihi, Loubna El Mansouri, Othmane Naggar

2020

Abstract

Hyperspectral imagery (HSI) is widely considered to be one of the most used technologies in different remote sensing applications, such as crop mapping, which provides an essential baseline for understanding and monitoring the Earth. Hyperspectral remote sensing, with its multiple narrow and continuous wavebands, allow significant improvements in the understanding of physiological processes of crops and the changes in their phenology, which are indistinct in multi-spectral remote sensing. A generous number of features can be derived from the hyperspectral data, although the classification of crops using high-dimensional and high-resolution data is a challenging task. The main objective of this paper is to list various techniques of machine learning mostly applied for hyperspectral data classification, besides the different hyperspectral open datasets mainly used in various researches.

Paper Citation

in Harvard Style

Karfi K., Fkihi S., Mansouri L. and Naggar O. (2020). Classification of Hyperspectral Remote Sensing Images for Crop Type Identification: State of the Art.In Proceedings of the 2nd International Conference on Advanced Technologies for Humanity - Volume 1: ICATH, ISBN 978-989-758-514-2, pages 11-18. DOI: 10.5220/0010426600110018

in Bibtex Style

@conference{icath20,
author={Kawtar El Karfi and Sanaa El Fkihi and Loubna El Mansouri and Othmane Naggar},
title={Classification of Hyperspectral Remote Sensing Images for Crop Type Identification: State of the Art},
booktitle={Proceedings of the 2nd International Conference on Advanced Technologies for Humanity - Volume 1: ICATH,},
year={2020},
pages={11-18},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010426600110018},
isbn={978-989-758-514-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advanced Technologies for Humanity - Volume 1: ICATH,
TI - Classification of Hyperspectral Remote Sensing Images for Crop Type Identification: State of the Art
SN - 978-989-758-514-2
AU - Karfi K.
AU - Fkihi S.
AU - Mansouri L.
AU - Naggar O.
PY - 2020
SP - 11
EP - 18
DO - 10.5220/0010426600110018