ASSESSMENT OF CROP AREA AND PRODUCTION OF MAJOR CROPS OF SEDAM TALUK OF GULBARGA USING REMOTE SENSING AND GIS
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Date
2015-10-10
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UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU
Abstract
Remote sensing is the art and science of making measurements of the earth using
sensors on airplanes or satellites. These sensors collect data in the form of images and
provide specialized capabilities for manipulating, analysing, and visualizing those
satellite images. Remote sensed imagery is integrated within a Geographic Information
System (GIS). Several methods exist for remote sensing image classification. They
include supervised and unsupervised approaches. In supervised classification, the
classifier is trained to identify the classes using training data set where as in an
unsupervised classification, the classifier itself develops the spectral classes. For
statistical comparisons of different image classification methods test imagery obtained
through IRS (Indian Remote Sensing) P6 LISS-III on 22nd December, 2011 for Sedum
Taluk of Gulbarga district of Karnataka state were used. Maximum likelihood
classification, Mahalanobis distance classification, Minimum distance to means
classification under supervised classification approach were performed for the test
imagery using ERDAS imagine 9.1 version software. Accuracy assessment was found
important to evaluate the final output of remote sensing. Accuracy of the classification of
each data set and their classifiers were expressed as an error matrix from which overall
accuracy, users accuracy, producers accuracy, mapping accuracy, F-measures, Kappa
coefficients and sample variance of Kappa coefficients were estimated. The test of
significance of Kappa coefficients were performed using Z-test. Pairwise comparison of
Kappa coefficients of different classification methods were performed. Maximum
likelihood classification was found to be the best with highest overall accuracy of 93.38
per cent for Sedum Taluk, Gulbarga district of Karnataka.
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