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.
|Title of host publication||ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing|
|Subtitle of host publication||Addressing Global Challenges through Automation and Computing|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 23 Oct 2017|
|Event||23rd IEEE International Conference on Automation and Computing, ICAC 2017 - Huddersfield, United Kingdom|
Duration: 7 Sept 2017 → 8 Sept 2017
|Name||ICAC 2017 - 2017 23rd IEEE International Conference on Automation and Computing: Addressing Global Challenges through Automation and Computing|
|Conference||23rd IEEE International Conference on Automation and Computing, ICAC 2017|
|Period||7/09/17 → 8/09/17|
Bibliographical noteACKNOWLEDGMENT 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.
© 2017 Chinese Automation and Computing Society in the UK - CACSUK.
- Machine learning
- Remote sensing
- Supervised classification