Volume IV-5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 415-420, 2018
https://doi.org/10.5194/isprs-annals-IV-5-415-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 415-420, 2018
https://doi.org/10.5194/isprs-annals-IV-5-415-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Nov 2018

15 Nov 2018

APPLICATION OF SUPPORT VECTOR MACHINES FOR FODDER CROP ASSESSMENT

S. Kala1, M. Singh1, S. Dutta2, N. Singh3, and S. Dwivedi1 S. Kala et al.
  • 1ICAR-National Dairy Research Institute, Karnal-132001, Haryana, India
  • 2Space Application Centre, ISRO, Jodhpur Tekra, Ambawadi Vistar, Ahmedabad-380015, India
  • 3Indian Institute of Technology (Indian School of Mines), Department of Mining Engineering, Dhanbad-826004, Jharkhand, India

Keywords: Fodder, SVM, MLC, Accuracy, Spectral-temporal, NDVI, Landsat-8

Abstract. Identification of crop and its accuracy is an important aspect in predicting crop production using Remote Sensing technology. This study investigates the ability of Support Vector Machine (SVM) algorithm in discriminating fodder crops and estimating its area using moderate resolution multi-temporal Landsat-8 OLI data. SVM is a non-parametric statistical learning method and its accuracy is dependent on the parameters and the kernels used. The objective was to evaluate the feasibility of SVM in fodder classification and compare the results with traditional parametric Maximum Likelihood Classification (MLC). Fodder crops are available over small fields in the study area thus having large number of pure fodder pixels over small area is difficult. Hence, SVM has an advantage over MLC as it works well with less training data sets also. Three kernels (linear, polynomial and radial based function) were used with SVM classification. Comparative analysis showed that higher overall accuracy was observed in SVM in comparison to MLC. Temporal change in the spectral properties of the crops derived through Normalized Difference Vegetation Index (NDVI) from multi-temporal Landsat-8 was found to be the most important information that affects accuracy of classification. The classification accuracies for SVM with radial based function, polynomial, linear kernel and MLC were 90.09%, 89.9%, 88.9% and 82.4% respectively. The result suggested that SVM including three kernels performed significantly better than MLC. India has low livestock productivity due to unavailability of fodder hence this study could help in strengthening the fodder productivity.