Unsupervised Satellite Image Segmentation by Combining SA Based Fuzzy Clustering with Support Vector Machine

Author

Mukhopadhyay, Anirban ; Maulik, Ujjwal

Author_Institution

Dept. of Comput. Sci. & Engg., Univ. of Kalyani, Kalyani

fYear

2009

fDate

4-6 Feb. 2009

Firstpage

381

Lastpage

384

Abstract

Fuzzy clustering is an important tool for unsupervised pixel classification in remotely sensed satellite images. In this article, a simulated annealing (SA) based fuzzy clustering method is developed and combined with popular support vector machine (SVM) classifier to fine tune the clustering produced by SA for obtaining an improved clustering performance. The performance of the proposed technique has been compared with that of some other well-known algorithms for an IRS satellite image of the city of Kolkata and its superiority has been demonstrated quantitatively and visually.

Keywords

fuzzy set theory; geophysical signal processing; image classification; image segmentation; pattern clustering; remote sensing; simulated annealing; support vector machines; fuzzy clustering; remotely sensed satellite images; simulated annealing; support vector machine; unsupervised pixel classification; unsupervised satellite image segmentation; Cities and towns; Clustering algorithms; Computer science; Encoding; Image segmentation; Pixel; Satellites; Simulated annealing; Support vector machine classification; Support vector machines; Fuzzy clustering; remote sensing images; simulated annealing; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Pattern Recognition, 2009. ICAPR '09. Seventh International Conference on

Conference_Location

Kolkata

Print_ISBN

978-1-4244-3335-3

Type

conf

DOI

10.1109/ICAPR.2009.50

Filename

4782814