Title :
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
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;
Conference_Titel :
Advances in Pattern Recognition, 2009. ICAPR '09. Seventh International Conference on
Conference_Location :
Kolkata
Print_ISBN :
978-1-4244-3335-3
DOI :
10.1109/ICAPR.2009.50
