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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
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: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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
DOI: 10.23919/IConAC.2017.8081990
URI: https://dspace.lboro.ac.uk/2134/25733
Publisher Link: https://doi.org/10.23919/IConAC.2017.8081990
ISBN: 9780701702601
Appears in Collections:Conference Papers and Presentations (Aeronautical and Automotive Engineering)

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