Publication Type:

Conference Paper

Source:

Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017, Institute of Electrical and Electronics Engineers Inc., Volume 2018-January, p.2272-2276 (2018)

ISBN:

9781509044412

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046350053&doi=10.1109%2fWiSPNET.2017.8300164&partnerID=40&md5=3597935c59a98f471de2814108514b1e

Keywords:

Airborne hyperspectral data, Classification (of information), Classification accuracy, Classification technique, Comparative analysis, Crop classification, Crops, Curse of dimensionality, High resolution data, Signal processing, Supervised and unsupervised classification, Wireless telecommunication systems

Abstract:

Crop classification using high-dimensional and high-resolution data is a challenging task. Though a large number of classes can be obtained from the hyperspectral data, the 'curse of dimensionality' causes the classification accuracy to be less than the expected value. A minimum noise transform has been applied to the data in this work, to reduce dimensionality and improve classification accuracy. This paper compares the different methods of supervised and unsupervised classification for the identification of different crops in a field. The results showed that it is better to use supervised methods over unsupervised as they yield better classification accuracy and kappa coefficient.

Notes:

cited By 0; Conference of 2nd IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017 ; Conference Date: 22 March 2017 Through 24 March 2017; Conference Code:134757

Cite this Research Publication

S. Reshma and S. Veni, “Comparative Analysis of Classification Techniques for Crop Classification using Airborne Hyperspectral Data”, in Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017, 2018, vol. 2018-January, pp. 2272-2276.