Land use/land cover classification using Sentinel-1 imagery and Support Vector Machines

  • Swati Suman
  • Prashant K Srivastava

Abstract

Describing the existing and quantifying the extent of
change in land use for spatially distributed land cover
pattern of a selected study site using different machine
based algorithms has been a topic of interest from a
long time. The main objective of the paper presented
here is appraisal of land use/land cover pattern for
Varanasi study site using Sentinel-1 images using pixel
based Support Vector Machines (SVMs) Polynomial algorithm.
SVMs, supervised image classifiers has been
particularly appealing in image classification studies
with its desirable trait to successfully handle small training
data and producing better accuracies than traditional
methods. The results indicated an overall accuracy
of 97.09% for the Sentinel-1 image classification
using SVMs, which confirms the suitability of the microwave
imagery like Sentinel-1 for land use/land cover
studies.

Published
2016-03-25
How to Cite
SUMAN, Swati; SRIVASTAVA, Prashant K. Land use/land cover classification using Sentinel-1 imagery and Support Vector Machines. Bulletin of Environmental and Scientific Research, [S.l.], v. 5, n. 1, p. 8-13, mar. 2016. ISSN 2278-5205. Available at: <http://besr.org.in/index.php/besr/article/view/63>. Date accessed: 17 sep. 2019.
Section
Articles