K-Means and ISODATA Clustering Algorithms for Landcover Classification Using Remote Sensing

Abstract

The aim of thisexploration work is to analyze the presentation ofunsupervised classification algorithms ISODATA(Iterative Self-Organizing Data Analysis Technique Algorithm)andK-Means in remote sensing, to evaluate statistically by iterative techniques to automatically group pixels of similar spectral features into unique clusters. This investigation used SUPARCO(Space and Upper Atmosphere Research Commission (Pakistan)) obtained remotely sensed patch of Abbottabad Pakistan. The test patch of Abbottabad is divided into Five bands i.e. NDVI (Normalized Difference Vegetation Index), green, near infrared, far infrared, and green. The ROIs (regions of interest) selected for classification of Land Cover data comprises five different types of classes i.e. water bodies, agriculture, settled area, forest and barren land. In this research of remote sensing the first step was to preprocess Abbottabad test patch by filtering, to improve performance of classification andneighboring pixels homogeneity. The next step was to assess the accuracy of Two pixel based unsupervised classifiers i.e. ISODATA and k-means on the said test patch. Finally, the mentioned classifiers performance is evaluated by varying their different parameters to categorize the effect of the clustering algorithms and their class statisticson whole classification outcomes.