A re-segmentation approach to detect rectangular objects in high resolution imagery. Thales Sehn Korting, Luciano Dutra, Leila Fonseca. GRSL, 2010 Image segmentation covers techniques for splitting one image into its components. This paper presents a re-segmentation approach applied to urban images. The interest elements, such as houses roofs, are considered to have a rectangular shape. Our technique finds and produces rectangular objects, setting to background the remaining elements to background. With an over-segmented image we connect bordering elements in a graph, known as {\it Region Adjacency Graph} – RAG. By going into the graph, we search for the best cuts that may result in more rectangular objects, using a relaxation-like approach. Results show that the method was able to match rectangles, according to user-defined parameters, such as maximum levels of graph depth search and minimum degree of rectangularity for interest objects.