PATTERN CLASSIFICATION OF RS AND GIS IMAGERY OF MADIKERI TALUK OF KODAGU DISTRICT OF KARNATAKA STATE
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Date
2019-08-24
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UNIVERSITY OF AGRICULTURAL SCIENCES, GKVK BENGALURU
Abstract
This study presents a land use, land cover classification of satellite imagery.
Visualization of feature space allows exploration of patterns in the imagery data. The
Machine learning algorithms are utilized for pattern classifications. Test imagery was
obtained through Sentinel-2 Satellite on February 2018 and February 2019 for Madikeri
Taluk, Kodagu District. The second image was taken to measure the changes due to
disaster happened in the study area in the month of August 2018.The supervised classifier
is identifying the classes using trained set. The statistical significance of satellite image
classifiers into constituent classes is of greater importance in remote sensing pattern
recognition methods. Maximum Likelihood Classification, Minimum Distance to means
Classification, Mahalanobis Distance Classification and Spectral Correlation Mapper
Classification were performed using ERDAS imagine and ArcGIS 10.5.1 image
processing Algorithms. Accuracy of the classification of data set and classifier were
expressed using confusion matrix. F-measure value and Kappa coefficients were used to
measure the overall accuracy, user’s accuracy, producer’s accuracy. The test of
significance of the Kappa coefficient was performed using Z- test. Maximum likelihood
classification was out performing with highest overall accuracy by 75.56 per cent
followed by Spectral correlation mapper 71.02 per cent, Minimum distance 67.61 per
cent and Mahalanobis distance 61.93 per cent were observed. The post disaster image
accuracy by the Maximum likelihood classifier is 73.29 per cent. The changes in the total
area among the feature after the disaster noticed was 1184 ha. This study guides the
Researchers and policymakers to study the pre and post disaster.