Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Tian-Xiang Zhang, Jin-Ya Su*, Cun-Jia Liu, Wen-Hua Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
2 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)16-26
Number of pages11
JournalInternational Journal of Automation and Computing
Volume16
Issue number1
Early online date27 Sep 2018
DOIs
Publication statusPublished - 1 Feb 2019

Keywords

  • image classification
  • precision agriculture
  • remote sensing
  • Sentinel-2A
  • supervised learning

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