Image Segmentation Techniques Using K-Means Clustering to Identify the Land Use Change Detection

Thechange detectionof the agriculture land and other landuseis one of the important application of remote sensing imagery.The major objective of this papertomeasurethe different boundary regionsof the land classes using an imagesegmentation techniques. The initial categorizing of different land use classes is experimented by using k-means clustering, which basically clusters the point of interest with the pixel similarity. The measurement of the different pixel region represent the different classes of agriculture area is achallenging task with thereal and synthetic images. The important characteristics of the algorithm preserves the cluster pixel details at most of the iterations, however for the similar canopy values the cluster effeminacyvaries and the identification of the land clusters also deviates as comparedwith the ground truth data