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.
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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.
Recommended by Associate Editor Jie Zhang
Tian-Xiang Zhang received the B. Eng. degree in flight vehicles design and engineering from Beijing Institue of Technology, China in 2015, and the M. Sc. degree in aerospace engineering from University of Manchester, UK in 2016. In January of 2017, he joined the Loughborough University Center for Autonomous System (LUCAS) Lab as a Ph. D. degree candidate in remote sensing applications for precision agriculture under the supervision of Dr. Cun-Jia Liu and Prof. Wen-Hua Chen, Department of Aeronautical and Automotive Engineering, Loughborough University, UK. He was fully funded by Chinese Scholarship Council (CSC) and now he is a member of the group joining the Newton Fund UK-China Agri-Tech Network Plus by Rothamsted Research on behalf of Science and Technology Facilities Council (STFC) and an IEEE student member.
His research interest is remote sensing applications for precision agriculture, including satellite and UAV image analysis, data assimilation adopting different methods for agricultural variables prediction and water stress analysis.
Jin-Ya Su received the B. Sc. degree in applied mathematics at School of Mathematics and Statistics, Shandong University, China in 2011, the Ph. D. degree in fault diagnosis at Department of Aeronautical and Automotive Engineering, Loughborough University, UK in 2016. Since 2015, he has been a research associate in Centre for Autonomous Systems, Loughborough University, UK. He received the Best Student Paper Award in the 19th International Conference on Automation and Computing (2013), the IEEE-IES Student Paper Travel Award in the 17th International Conference on Industrial Technology (2016), and the Annual ICI Prize from the Institute of Measurement and Control in 2016. In 2015, he received the prestigious Chinese Government Award for Outstanding Self-financed Students Abroad.
His research interests include Kalman filter, machine learning and their applications to autonomous systems such as intelligent vehicle, agricultural information system.
Cun-Jia Liu received the B. Eng. and M. Sc. degrees in guidance, navigation, and control from Beihang University, China in 2005 and 2008, respectively, and the Ph. D. degree in autonomous vehicle control from Loughborough University, UK in 2011. He has been a lecturer of flight dynamics and control with Loughborough University, since 2013.
His research interests include optimization-based control, disturbance-observer-based control, Bayesian information fusion, and their applications to autonomous vehicles for flight control, path planning, decision making, and situation awareness.
Wen-Hua Chen received the M. Sc. and Ph. D. degrees in control engineering from Northeast University, China in 1989 and 1991, respectively. From 1991 to 1996, he was with Department of Automatic Control, Nanjing University of Aeronautics and Astronautics, China. From 1997 to 2000, he was a researcher and then a lecturer of control engineering with the Centre for Systems and Control, University of Glasgow, UK. In 2000, he became a lecturer with Department of Aeronautical and Automotive Engineering, Loughborough University, UK, where he was appointed as a professor in 2012. He is a chartered engineer (CEng) in the UK, a fellow of Institute of Electrical and Electronics Engineering (FIEEE), a fellow of the Institution of Engineering and Technology (FIET) and a fellow of the Institution of Mechanical Engineers (FIMechE).
His research interests include development of advanced control strategies, such as non-linear model predictive control and disturbance-observer-based control, and their applications in aerospace and automotive engineering, and development of unmanned autonomous intelligent systems.
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Zhang, T., Su, J., Liu, C. et al. Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture. Int. J. Autom. Comput. 16, 16–26 (2019). https://doi.org/10.1007/s11633-018-1143-x
- remote sensing
- image classification
- supervised learning
- precision agriculture