Perspectives in Science · 2016 · 22 citations · 2 references
ComputingEarth SciencesGeoinformaticsComputer VisionGeospatial Artificial IntelligenceRemote SensingChange DetectionImage ClassificationLand Cover ClassificationRemote Sensing Change DetectionRemote Sensing Image AnalysisRemote Sensing Of IceChange Detection From Multispectral ImageryClassification AlgorithmsGlacier Remote SensingRemote Sensing Of Glacier ChangeSub-pixel AnalysisSub-pixel Change DetectionSupervised ClassificationSupervised Classification Algorithms
Main aim of this paper is to compare the classification accuracies of glacier change detection by following classifiers: sub-pixel classification algorithm, indices based supervised classification and object based algorithm using Landsat imageries. It was observed that shadow effect was not removed in sub-pixel based classification which was removed by the indices method. Further the accuracy was improved by object based classification. Objective of the paper is to analyse different classification algorithms and interpret which one gives the best results in mountainous regions. The study showed that object based method was best in mountainous regions as optimum results were obtained in the shadowed covered regions.
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