Title :
One-class support vector machines based cluster validity in the segmentation of hyperspectral images
Author :
Bilgin, Gökhan ; Ertürk, Sarp ; Yildirim, T.
Abstract :
In this paper, a novel cluster validation method based on one-class support vector machines (OC-SVM )is presented. Also it is proposed to segment hyperspectral images with subtractive clustering accompanied by phase correlation. The proposed cluster validity measure is based on the power of spectral discrimination (PWSD) measure and utilizes the advantage of the inherited cluster contour definition feature of OC-SVM. Basically this method provides a solution to the estimation of the correct number of clusters which is an important problem in hyperspectral image segmentation.
Keywords :
image segmentation; pattern clustering; support vector machines; cluster validation method; hyperspectral image segmentation; one class support vector machine; phase correlation; spectral discrimination; Hyperspectral imaging; Image segmentation; Power measurement; Support vector machines; Testing;
Conference_Titel :
Signal Processing and Communications Applications Conference, 2009. SIU 2009. IEEE 17th
Conference_Location :
Antalya
Print_ISBN :
978-1-4244-4435-9
Electronic_ISBN :
978-1-4244-4436-6
DOI :
10.1109/SIU.2009.5136522
