Segmentation of remote-sensing images by incremental neural network

Authors:
Mehmet Nadir Kurnaz;Zümray Dokur;Tamer Ölmez
Affiliations:
Department of Electronics and Communication Engineering, Istanbul Technical University, 80626 Maslak, Istanbul, Turkey;Department of Electronics and Communication Engineering, Istanbul Technical University, 80626 Maslak, Istanbul, Turkey;Department of Electronics and Communication Engineering, Istanbul Technical University, 80626 Maslak, Istanbul, Turkey
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

In this study, a novel incremental neural network (INeN) is proposed for the segmentation of remote-sensing images. The data set consists of seven images acquired by the Landsat-5 TM sensor. Two feature extraction methods are comparatively examined for the segmentation of the remote-sensing images. In the first method, features are formed by the intensity of one pixel of each channel. In the second method, intensities at one neighborhood of the pixel are used to form the feature vectors. In this study, the INeN and the Kohonen network are employed for the segmentation of the remote-sensing images. The INeN is proposed to determine the number of classes automatically.