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Machine Learning Technique Approaches Versus Statistical Methods in Classification of Multispectral Remote Sensing Data using Maximum Likelihood Classification: Koluru Hobli, Bellary Taluk, District, Karnataka, India


 
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1. Title Title of document Machine Learning Technique Approaches Versus Statistical Methods in Classification of Multispectral Remote Sensing Data using Maximum Likelihood Classification: Koluru Hobli, Bellary Taluk, District, Karnataka, India
 
2. Creator Author's name, affiliation, country S. S. Patil; Department of Computer Science, University of Agricultural Sciences, Bangalore, Karnataka, India
 
2. Creator Author's name, affiliation, country Sachidananda .; Department of Computer Science, University of Agricultural Sciences, Bangalore, Karnataka, India
 
2. Creator Author's name, affiliation, country U. B. Angadi; CABin, ISRI, New Delhi, India
 
2. Creator Author's name, affiliation, country D. K. Prabhuraj; KSRSAC, Bangalore, Karnataka, India
 
3. Subject Discipline(s) Computer Science, Image Processing
 
3. Subject Keyword(s) Machine Learning Techniques; Supervised Classification; Maximum Likelihood Classification; Kappa Coefficient; Classification Accuracy; f-measure
 
3. Subject Subject classification Computer Science, GIS
 
4. Description Abstract Classification is challenging task on complex features of Remote sensing satellite imageries color pixels variability of patterns. Machine learning techniques have delivered the improved in accuracy of classification of patterns of features. Remote sensing color based imageries having hard to cluster color pixels with variability in intensity of colors. Challenges in estimation of various features viz, crop fields, fallow land, buildings, roads, rivers, water bodies, forest, and other trivial items. Urge in estimation of crop yield predictions through satellite imageries. We are attempted to converging accuracy of estimation of vegetation crop yield of fields. Kappa coefficient to achieve high degree accuracy estimation of crop wise with suitable thresh hold to ground truth data.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2014-04-28
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type image classification
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier http://technical.cloud-journals.com/index.php/IJARSG/article/view/Tech-249
11. Source Journal/conference title; vol., no. (year) International Journal of Advanced Remote Sensing and GIS; Volume 3 (Year 2014)
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.) 150 9΄ to 150 15΄N latitude and 760 55΄ to 760 92΄E longitudes.,
of 21st November, 2010,
satellit imageries
 
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