Segmentation of Hyperspectral Image Using JSEG Based on Unsupervised Clustering Algorithms

Hyperspectral image analysis is a complicated and challenging task due to the inherent nature of the image. The main aim of this work is to segment the object in hyperspectral scene using image processing technique. This paper address a novel approach entitled as Segmentation of hyperspectral image using JSEG based on unsupervised cluster methods. In the preprocessing part, single band is picked out from the hyperspectral image and then converts into false color image. The JSEG algorithm is segregate the false color image properly without manual parameter adjustment. The segmentation has carried in two major stages. To begin with, colors in the image are quantized to represent several classes which can be used to differentiate regions in the image. Besides, hit rate regions with cognate color regions merging algorithm is used. In region merging part, K-means, Fuzzy C-Means (FCM) and Fast K-Means weighted option (FWKM) algorithm are used to segregate the image in accordance with the color for each cluster and its neighborhoods. Experiment results of above clustering method could be analyzed in terms of mean, standard deviation, number of cluster, number of pixels, time taken, number of objects occur in the resultant image. FWKM algorithm results yields good performance than its counterparts.