Refining the histogram based segmentation of hyperspectral data

A recently-developed technique of histogram-based segmentation of hyperspectral data allows for a plethora of segmentations. The user can specify the desired number of levels of segmentations, minimum number of pixels defining a peak, and degree of non-linearity in mapping from principal component floating values to histogram bins, all of which affect the derived segmentation. In the present work, we seek to extend previous work which arrives at a small range of clusters or segmentation levels from the image itself. We seek within this range to find "better" segmentations or possibly a unique representative segmentation. The method employed to achieve this goal starts with an over-fine segmentation, i.e. more segmentation levels than needed, and uses quantitative metrics to measure the "quality" of that segmentation and to guide a compression into a reduced segmentation. If the method has merit, different starts should compress down into comparable segmentations. Therefore a measure to establish the similarity of two or more segmentations was developed. Different quantitative metrics were studied and several modes of compression were examined. Some impressive results are presented but the methods are still not robust with respect to segmentation starts and are image dependent as to the best modes of compression.