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

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3 Author(s)
Korting, T.S. ; Image Process. Div. (DPI), Nat. Inst. for Space Res. (INPE), São José dos Campos, Brazil ; Dutra, L.V. ; Fonseca, L.M.G.

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.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:8 ,  Issue: 4 )