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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2010.2098389