Local integrity constraints for structure detection and segmentation in high-resolution earth observation images

Considering the idea that objects in images have a higher local structural integrity than the background they lie into, we propose a method that learns a supervised distance characterizing the membership of a pair of elements to the target structure. We test our ideas by applying them to the task of extracting semantic structures in high resolution Earth Observation images. The results show that the method works well in many situations when there is no training dataset. The limits of the method are also discussed.