Multisensor data fusion by a region-based approach

A new approach is proposed to the fusion of 2D images, called the region overlapping technique. After registration of the original images, the single channel segmented images are overlapped and a new segmentation is obtained, where regions are labeled as kernel or silver on the basis of region parameters such as size, elongatedness, and adjacency. The knowledge-based (KB) interpretation of kernel regions is carried out by matching regions with models; sliver regions are interpreted in a second phase, considering their possible sources, provenance in the two original images, and the kernel interpretation results. Experimental results obtained both in the medical and in the remote sensing fields are reported and discussed