Band selection in sentinel-2 satellite for agriculture applications

Tianxiang Zhang, Jinya Su, Cunjia Liu, Wen Hua Chen, Hui Liu, Guohai Liu

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

43 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing
Subtitle of host publicationAddressing Global Challenges through Automation and Computing
EditorsJie Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780701702618
DOIs
Publication statusPublished - 23 Oct 2017
Event23rd IEEE International Conference on Automation and Computing, ICAC 2017 - Huddersfield, United Kingdom
Duration: 7 Sep 20178 Sep 2017

Publication series

NameICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing

Conference

Conference23rd IEEE International Conference on Automation and Computing, ICAC 2017
Country/TerritoryUnited Kingdom
CityHuddersfield
Period7/09/178/09/17

Keywords

  • Agriculture
  • Machine learning
  • Remote sensing
  • Sentinel-2A
  • Supervised classification

Fingerprint

Dive into the research topics of 'Band selection in sentinel-2 satellite for agriculture applications'. Together they form a unique fingerprint.

Cite this