Support vector machine for classification of various crop using high resolution LISS-IV imagery

  • Pradeep Kumar
  • Rajendra Prasad
  • Dileep Kumar Gupta
  • Varun Narayan Mishra
  • Arti Choudhary

Abstract

The Resourcesat-2 is an exceedingly suitable satellite
with its improved features and capabilities for crop
classification studies. Data from one of its sensors, the
Linear Imaging Self-Scanning (LISS-IV), which has a
spatial resolution of 5.8 m, was used for the classification
of various crop and non-crop in Varanasi district,
Uttar Pradesh, India. The imagery was classified into
classes of crop such as corn, linseed, lentil, mustard,
barley, wheat, pigeon pea, sugarcane, pea and other
crops and non-crop such as fallow land, sparse vegetation,
dense vegetation, sand, built up, andwater classes.
The overall accuracies achieved by support vector machine
(SVM) with polynomial of degrees 3, 4, 5 and 6
were 87.77%, 87.96%, 88.15% and 88.15% and kappa
(·) 0.8686, 0.8706, 0.8726 and 0.8726 respectively. Results
derived from SVM with different degree polynomialswere
validated with the ground truth information acquired
by the field visit on 6 April 2013.

Published
2015-10-10
How to Cite
KUMAR, Pradeep et al. Support vector machine for classification of various crop using high resolution LISS-IV imagery. Bulletin of Environmental and Scientific Research, [S.l.], v. 4, n. 3, p. 1-5, oct. 2015. ISSN 2278-5205. Available at: <http://besr.org.in/index.php/besr/article/view/50>. Date accessed: 17 sep. 2019.
Section
Articles