Feature Extraction Approach in Hyperspectral Data
- https://doi.org/10.2991/aer.k.211029.019How to use a DOI?
- Feature extraction; hyperspectral data; principal component; meaningful features
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.
- © 2021 The Authors. Published by Atlantis Press International B.V.
- Open Access
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Cite this article
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 - https://doi.org/10.2991/aer.k.211029.019 ID - Altangerel2021 ER -