Hyperspectral Image Segmentation Based on Enhanced Estimation of Centroid with Fast K-Means

In this paper, the segmentation process is observant on hyperspectral satellite images. A novel approach, hyperspectral image segmentation based on enhanced estimation of centroid with unsupervised clusters such as fast k-means, fast k-means (weight), and fast k-means (careful seeding) has been addressed. Besides, a cohesive image segmentation approach based on inter-band clustering and infra-band clustering is processed. Moreover, the inter band clustering is accomplished by above clustering algorithms, while the infra band clustering is effectuated using Particle Swarm Clustering algorithm (PSC) with Enhanced Estimation of Centroid (EEOC). The hyperspectral bands are clustered and a single band which has a paramount variance from each cluster is opting for. This constructs the diminished set of bands. Finally, PSC EEOC carried out the segmentation process on the diminished bands. In addition, we compare the result produce in these methods by statistical analysis based on number of pixel, fitness value, and elapsed time.