Hyperspectral
Image Segmentation Based on Enhanced Estimation of Centroid with Fast K-Means
Saravana Kumar Veligandan1 and Naganathan Rengasari2
1Department of Information Technology, SreeNidhi Institute of
Science and Technology, India
2Symbiosis Institute of Computer Studies and Research,
Symbiosis International (Deemed University), India
Abstract:
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
intra-band clustering is processed. Moreover, the inter band clustering is
accomplished by above clustering algorithms, while the intra 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.
Keywords: Fast k-means, fast k-mean (weight),
fast k- means (careful seeding), and particle swarm clustering algorithm.