Edge Detection of Multispectral Images Using the 1-D Self-Organizing Map

Abstract

In this paper, a new method for edge detection in multispectral images is presented. It is based on the use of the Self-Organizing Map (SOM) and a conventional edge detector. The method presented in this paper orders the vectors of the original image in such a way that vectors that are near each other according to some similarity criterium should have scalar ordering values near each other. This is achieved using the 1-dimensional Self-Organizing Map. After ordering, the original vector image reduces to a gray-value image, and conventional edge detectors can be applied. In this paper, the Laplace and Canny edge detectors are used. It is shown, that using the Self-Organizing Map (SOM) in ordering the vectors of the original spectral image it is possible to find also those edges that the R-ordering based methods miss.