Zhang2018_Article_PotentialBandsOfSentinel-2ASat.pdf (1.92 MB)

Potential bands of sentinel-2A satellite for classification problems in precision agriculture

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journal contribution
posted on 26.10.2018, 10:42 by Tiangxiang Zhang, Jinya Su, Cunjia Liu, Wen-Hua Chen
Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices, such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress (e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes (crop, tree, soil, water and road) with the support vector machines (SVMs) algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands (red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information (MI), and full bands of on board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.

Funding

This work was supported by Science and Technology Facilities Council (STFC) under Newton fund (No. ST/N006852/1). Tian-Xiang Zhang would also like to thank Chinese Scholarship Council (CSC) for supporting his study in the UK.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

International Journal of Automation and Computing

Citation

ZHANG, T. ... et al., 2019. Potential bands of sentinel-2A satellite for classification problems in precision agriculture. International Journal of Automation and Computing, 16(1), pp 16–26.

Publisher

Springer © The Author(s)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

26/06/2018

Publication date

2019

Notes

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

ISSN

1476-8186

eISSN

1751-8520

Language

en

Licence

Exports