Feature Extraction Approach in Hyperspectral Data

DOI

10.2991/aer.k.211029.019How to use a DOI?

Keywords

Feature extraction; hyperspectral data; principal component; meaningful features

Abstract

In general, feature extraction deals with the problem of finding the most informative, distinctive, and reduced set of features and improve the success of data processing. The features should contain information required to distinguish between classes, be insensitive to irrelevant variability in the input, and also be limited in number, to permit, efficient computation of the applied functions and to limit the amount of data required. In many cases, it is an important step in the solutions of many tasks aiming to extract the relevant information from the available large datasets. The aim of this study is to apply a feature extraction approach to a hyperspectral image and extract different features from the dataset and reduce its dimensionality into meaningful orthogonal features. The final analysis was performed in a test site situated in central Mongolia using 242 band Hyperion data. Overall, the study indicated that the Hyperion hyperspectral data could be effectively reduced into meaningful features through a feature extraction process.

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  - Munkh-Erdene Altangerel
AU  - Amarsaikhan Damdinsuren
AU  - Enkhjargal Damdinsuren
AU  - Odontuya Gendaram
AU  - Jargaldalai Enkhtuya
PY  - 2021
DA  - 2021/11/01
TI  - Feature Extraction Approach in Hyperspectral Data
BT  - Proceedings of the Environmental Science and Technology International Conference (ESTIC 2021)
PB  - Atlantis Press
SP  - 102
EP  - 108
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.211029.019
DO  - 10.2991/aer.k.211029.019
ID  - Altangerel2021
ER  -