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Classification of multispectral satallite images by using adaptive neuro-fuzzy classifier with linguistic hedges

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2 Author(s)
Cetisli, B. ; Bilgisayar Muhendisligi Bolumu, Suleyman Demirel Univ., Isparta, Turkey ; Kalkan, H.

In this study, vegetation species were classified by using multispectral satellite images. A full wavelet transform is used to decompose the images into sub-images and the energy in each sub-images is assigned as feature for classification. These features were eliminated and classified by using neuro-fuzzy classifier with linguistic hedges. A classification accuracy of 93.75% was achieved by using the selected five features among 252 extracted features.

Published in:

Signal Processing and Communications Applications (SIU), 2011 IEEE 19th Conference on

Date of Conference:

20-22 April 2011

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