Edge detection in multispectral images using the n-dimensional self-organizing map

We propose a new method for performing edge detection in multi-spectral images based on the self-organizing map (SOM) concept. Previously, 1-dimensional or 2-dimensional SOMs were trained to provide a linear mapping of high-dimensional multispectral vectors. Then, edge detection was applied on that mapping. However, the 1-dimensional SOM may not converge on a suitable global order for images with rich content. Likewise, the 2-dimensional SOM introduces false edges due to linearization artifacts. Our method feeds the edge detector without linearization. Instead, it exploits directly the distances of SOM neurons. This avoids the aforementioned drawbacks and is more general, as a SOM of arbitrary dimensionality can be used. We show that our method achieves significantly better edge detection results than previous work on a high-resolution multispectral image database.