Segmentation of multi-spectral images using the combined classifier approach

Segmentation methods, combining spectral and spatial information, are essential for analysis of multi-spectral images. In this article, we propose such a method based on statistical pattern recognition algorithms and a combined classifier approach. A set of experiments is presented with multi-spectral images of detergent laundry powders acquired by imaging cross-sections with scanning electron microscopy using energy-dispersive X-ray microanalysis (SEM/EDX). The algorithm stability and the segmentation quality are investigated. The use of apriori information for the segmentation of images with similar spectral properties is studied as well. Finally, a comparison with probabilistic relaxation method for multi-spectral image segmentation is made.