Affiliations: Department of Computer Science and Engineering,
University of Kalyani, Kalyani - 741235, India. anirbanbuba@yahoo.com | Department of Computer Science and Engineering,
Jadavpur University, Kolkata - 700032, India. drumaulik@cse.jdvu.ac.in
Note: [] Address for correspondence: Department of Computer Science and
Engineering, University of Kalyani, Kalyani - 741235, India
Abstract: A popular approach for landcover classification in remotely sensed
satellite images is clustering the pixels in the spectral domain into several
fuzzy partitions. It has been observed that performance of the clustering
algorithms deteriorate with more and more overlaps in the data sets. Motivated
by this observation, in this article a two-stage fuzzy clustering algorithm is
described that utilizes the concept of points having significant membership to
multiple classes. The points situated in the overlapped regions of different
clusters are first identified and excluded from consideration while clustering.
Thereafter, these points are given class labels based on Support vector Machine
classifier which is trained by the remaining points. The well known Fuzzy
C-Means algorithm and some recently proposed genetic clustering schemes are
utilized in the process. The effectiveness of the two-stage clustering
technique has been demonstrated on IRS remote sensing satellite images of the
cities of Bombay and Calcutta and compared with other well known clustering
techniques. Also statistical significance test has been carried out to
establish the statistical significance of the clustering results.
