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Unsupervised Training for Image Segmentation via Spatial Clustering and Nonparametric Spectral Distance Measures

A new region growing procedure has been developed which is based on a nonstandard implementation of a pyramid data structure and nonparametric distance measures to group pixels into regions. Unlike traditional region growing procedures, this method uses texture transforms instead of the original spectral bands. A nearest neighbor matching procedure based on spectral means of regions has also been developed to classify the region output. The resulting classification is then used to train a supervised maximum likelihood classifier. This investigation was originally motivated by the fact that image segmentation is often required for areas where ground truth cannot be acquired. For example, studies are often conducted to monitor change using imagery acquired at some previous time. It is impossible to go to the field to acquire ground truth for images that are five or ten years old. In other cases, it may be impractical (due to terrain or climate) or simply too costly to acquire ground truth. An automated training procedure would greatly facilitate analysis of the information in these images.