A Resegmentation Approach for Detecting Rectangular Objects in High-Resolution Imagery

• Published in: IEEE geoscience and remote sensing letters 2011-07, Vol.8 (4), p.621-625
• Main Authors: Korting, Thales Sehn, Dutra, Luciano Vieira, Garcia Fonseca, Leila Maria
• Format: Article
• Language: English
• Subjects: Algorithms; Borders; Graph theory; Graphs; image classification; Image edge detection; Image segmentation; Manuals; Merging; Pixel; Rectangles; Remote sensing; Shape; Similarity; Spectra; Splitting; Studies
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2010.2098389

Image segmentation covers techniques for splitting one image into its components as homogeneous regions. This letter presents a resegmentation approach applied to urban images. Resegmentation represents the set of adjustments from a previous segmentation in which the elements are small regions with a high degree of spectral similarity (a condition known as oversegmentation). The focus of this letter is the house roofs, which are assumed to have a rectangular shape. These regions are merged according to an objective function, which, in the technique presented here, maximizes the rectangularity. With oversegmentation, we create a graph known as a region adjacency graph (RAG) that relates border elements. The main contribution of this letter is a technique, which works with the RAG, to maximize the objective function in a relaxationlike approach that splits and merges oversegmented regions until they form a meaningful object. The results showed that the method was able to detect rectangles according to user-defined parameters, such as the maximum level of the graph depth and the minimum degree of rectangularity for objects of interest.