SOM‘s and Rule based Classification of a very High Resolution Images using Multispectral Segmentation

There are many segmentation algorithms in the literature, but few have been applied in urban studies to classify a highspatial- resolution remote-sensing image. Furthermore, the user must specify the spectral and spatial parameters that are data dependent. In this paper, we propose an automatic multispectral segmentation algorithm inspired by the specific idea of guiding a classification process for a high-spatial-resolution remote-sensing image of an urban area using an existing digital map of the same area using SOM‘s and rule based classification. Kohonen‘s self-organizing maps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results.