Improved tree-structured segmentation of remote sensing images
The Bayesian/MRF approach guarantees high-quality image segmentation, at the price of a significant computational cost. To limit complexity, one can resort to the recently proposed tree-structured MRF-based segmentation, which converts a Kclass problem in a sequence of much simpler binary tasks. The binary-tree structure imposed to the image, however, can also reduce segmentation accuracy. Here we propose an improved tree-structured segmentation algorithm, where disjoint regions, even belonging to the same class, are immediately split, giving rise to a generic tree structure, and are grouped again in classes only at the end of the algorithm. As a result, both segmentation speed and accuracy increase.