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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25733

Title: Band selection in Sentinel-2 satellite for agriculture applications
Authors: Zhang, Tianxiang
Su, Jinya
Liu, Cunjia
Chen, Wen-Hua
Liu, Hui
Liu, Guohai
Keywords: Remote sensing
Machine learning
Sentinel-2A
Agriculture
Supervised classification
Issue Date: 2017
Publisher: © IEEE
Citation: ZHANG, T. ... et al, 2017. Band selection in Sentinel-2 satellite for agriculture applications. 2017 23rd International Conference on Automation & Computing (ICAC), Huddersfield, UK, 7th-8th September 2017.
Abstract: Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices, such as NDVI and NDWI, are defined based on the sensitivity and significance of specific bands. Nowadays, remote sensing capability with a good number of bands and high spatial resolution is available. Instead of classification based on indices, this paper explores direct classification using selected bands. Recently launched Sentinel-2A is adopted as a case study. Three methods are compared, where the first approach utilizes traditional indices and the latter two approaches adopt specific bands (Red, NIR, and SWIR) and full bands of on-board sensors, respectively. It is shown that a better classification performance can be achieved by directly using the three selected bands compared with the one using indices, while the use of all 13 bands can further improve the performance. Therefore, it is recommended the new approach can be applied for Sentinel-2A image analysis and other wide applications.
Description: This conference paper is closed access until it is published.
Sponsor: This work was supported by Newton Fund UK-China Agri-Tech Network Plus which is managed by Rothamsted Research on behalf of Science and Technology Facilities Council (STFC). Tianxiang Zhang would also like to thank Chinese Scholarship Council (CSC) for supporting his study in the U.K.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/25733
Appears in Collections:Closed Access (Aeronautical and Automotive Engineering)

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