Comparison of Spectral Signatures in Hyperspectral and Multispectral Data

DOI

10.2991/aer.k.211029.021How to use a DOI?

Keywords

Hyperspectral; multispectral; spectral signatures; land surface features

Abstract

Since the launch of the first Earth observation satellites, multispectral remote sensing (RS) datasets have been efficiently used for different thematic applications. The basis of the thematic studies is the analysis of reflecting, absorbing and emitting properties of different land surface features at different wavelengths of electro-magnetic spectrum. In other words, one should differentiate the features by their spectral signatures as observed in original data. Recently, hyperspectral datasets have been widely used for a variety of different applications. They have a number of advantages compared to multispectral data for the identification and discrimination of the features. For example, hyperspectral images have a significant adavantage over the trandtional optical images in land cover mapping, color enhancement of the Earth’s objects and many others. The aim of this research is to compare the spectral signature characteristics of different land surface features in hyperspectral and multispectral images. For this purpose, Hyperion and Sentinel-2 images of Mongolia have been used. The results indicated that compared to the traditional multichannel data, the hyperspectral image could accurately differentiate the spectral characteristics of similar land cover types.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Odontuya Gendaram
AU  - Amarsaikhan Damdinsuren
PY  - 2021
DA  - 2021/11/01
TI  - Comparison of Spectral Signatures in Hyperspectral and Multispectral Data
BT  - Proceedings of the Environmental Science and Technology International Conference (ESTIC 2021)
PB  - Atlantis Press
SP  - 116
EP  - 120
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.211029.021
DO  - 10.2991/aer.k.211029.021
ID  - Gendaram2021
ER  -