Segmentation of hyperspectral images using self-organizing maps

Hyperspectral image analysis has been attracting research attention in a variety of fields. Since the size of hyperspectral data cubes can easily reach gigabytes, their efficient transfer, manual delineation, and intrinsic heterogeneity have become serious obstacles in building ground-truth datasets in emerging scenarios. Therefore, applying supervised learners for the hyperspectral classification and segmentation remains a difficult yet very important task in practice, as segmentation is a pivotal step in the process of extracting useful information about the scanned area from such highly dimensional data. We tackle this problem using self-organizing maps and exploit an unsupervised algorithm for segmenting such imagery. The experimental study, performed over two benchmark hyperspectral scenes and backed up with the sensitivity analysis, showed that our technique can be applied for this purpose due to its flexibility, it delivers reliable segmentations, and offers fast operation.