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Title: Potential bands of sentinel-2A satellite for classification problems in precision agriculture
Authors: Zhang, Tianxiang
Su, Jinya
Liu, Cunjia
Chen, Wen-Hua
Keywords: Sentinel-2A
Remote sensing
Image classification
Supervised learning
Precision agriculture
Issue Date: 2018
Publisher: Springer © The Author(s)
Citation: ZHANG, T. ... et al., 2018. Potential bands of sentinel-2A satellite for classification problems in precision agriculture. International Journal of Automation and Computing, DOI: 10.1007/s11633-018-1143-x
Abstract: 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.
Description: 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.
Sponsor: 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.
Version: Published
DOI: 10.1007/s11633-018-1143-x
URI: https://dspace.lboro.ac.uk/2134/35502
Publisher Link: https://doi.org/10.1007/s11633-018-1143-x
ISSN: 1476-8186
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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