REFINING IMAGE SEGMENTATION BY INTEGRATION OF EDGE AND REGION DATA

A basic requirement for understanding the dynamics of the Earth's major ecosystems is accurate quantitative information about the distribution and areal extent of the Earth's vegetation formations, Some of this required information can be obtained through the analysis of remotely sensed data, Image segmentation is often one of the first steps of this analysis, This paper focuses on two particular types of segmentation: region-based and edge-based segmentations, Each approach is affected differently by various factors, and both types of segmentations may be improved by taking advantage of their complementary nature, Included among region-based segmentation approaches are region growing methods, which produce hierarchical segmentations of images from finer to coarser resolution. In this hierarchy, an ideal segmentation (ideal for a given application) does not always correspond to one single iteration, but may correspond to several different iterations, This, among other factors, makes it somewhat difficult to choose a stopping criterion for region growing methods, To find the ideal segmentation, we develop a stopping criterion for our Iterative Parallel Region Growing (IPRG) algorithm using additional information from edge features, and the Hausdorff distance metric, We integrate information from regions and edges at the symbol level, taking advantage of the hierarchical structure of the region segmentation results, Also, to demonstrate the feasibility of this approach in processing the massive amount of data that will be generated by future Earth remote sensing missions, such as the Earth Observing System (EOS), all the different steps of this algorithm (namely, region growing, edge detection, Hausdorff distance computation, and edge/region fusion) have been implemented on a massively parallel processor.