Building detection using enhanced HOG-LBP features and region refinement processes

Konstantinidis, Dimitrios, Stathaki, Tania, Argyriou, Vasileios and Grammalidis, Nikolaos (2016) Building detection using enhanced HOG-LBP features and region refinement processes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN (print) 1939-1404 (Epub Ahead of Print)

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Abstract

Building detection from 2D high-resolution satellite images is a computer vision, photogrammetry and remote sensing task that has arisen in the last decades with the advances in sensors technology and can be utilised in several applications that require the creation of urban maps or the study of urban changes. However, the variety of irrelevant objects that appear in an urban environment and resemble buildings and the significant variations in the shape and generally the appearance of buildings render building detection a quite demanding task. As a result, automated methods that can robustly detect buildings in satellite images are necessary. To this end, we propose a building detection method that consists of two modules. The first module is a feature detector that extracts Histograms of Oriented Gradients (HOG) and Local Binary Patterns (LBP) from image regions. Using a novel approach, a Support Vector Machine (SVM) classifier is trained with the introduction of a special denoising distance measure for the computation of distances between HOG-LBP descriptors before their classification to the building or non-building class. The second module consists of a set of region refinement processes that employs the output of the HOG-LBP detector in the form of detected rectangular image regions. Image segmentation is performed and a novel building recognition methodology is proposed to accurately identify building regions, while simultaneously discard false detections of the first module of the proposed method. We demonstrate that the proposed methodology can robustly detect buildings from satellite images and outperforms state-of-the-art building detection methods.

Item Type: Article
Additional Information: This work was supported by the European Union (European Social Fund-ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)-Research Funding Program: THALIS-NTUAUrbanMonitor.
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing > Digital Imaging Research Centre (DIRC)
Depositing User: Vasileios Argyriou
Date Deposited: 17 Aug 2016 07:26
Last Modified: 12 Jan 2017 12:02
URI: http://eprints.kingston.ac.uk/id/eprint/35697

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