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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2017: 100.00
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2019.8(10): 2634-2643
DOI: https://doi.org/10.20546/ijcmas.2019.810.304


Evaluation of the Performance of Supervised Classification Alogorithums in Image Classification
Jhade Sunil* and Abhishek Singh
Department of Farm Engineering (Agricultural Statistics), Banaras Hindu University, Varanasi, India
*Corresponding author
Abstract:

This study presents a land use pattern classification of satellite imagery. The Machine learning algorithms are overseen to pattern classifications. The supervised classifier is identifying the classes using trained set. Compiled classification has to be improvised using efficient algorithms with appropriate threshold values. The statistical significance of satellite image classifies into essential classes is of greater importance in remote sensing pattern classification methods. Test imagery were obtained through Sentinel-2B Satellite on 15th January 2018 for Ambaji Durga Hobli, Chikkaballapur District. Maximum Likelihood Classification, Minimum Distance to means Classification, Mahalanobis Distance Classification, Spectral Correlation Mapper Classification were performed using ArcGIS 10.5.1 and ERDAS 2015 imagine image processing soft wares. Accuracy of the classification expressed using confusion matrix. The measures such as overall accuracy, F-measure value, Kappa coefficients its variance were estimated. The test of significance of the Kappa coefficient was performed using Z- test. Maximum likelihood classification out performed with highest overall accuracy of 72.99 per cent followed by Minimum distance to mean 68.61 per cent, Mahalanobis distance 61.31 per cent, Spectral correlation mapper 56.20 per cent This study helps the farmers using early and accurate estimates of yields, estimate area of crop production.


Keywords: Remote sensing; Land use pattern; Supervised Classification; Classification Accuracy; kappa coefficient
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How to cite this article:

Jhade Sunil and Abhishek Singh. 2019. Evaluation of the Performance of Supervised Classification Alogorithums in Image Classification.Int.J.Curr.Microbiol.App.Sci. 8(10): 2634-2643. doi: https://doi.org/10.20546/ijcmas.2019.810.304