Object Classification using Hybrid Holistic Descriptors: Application to Building Detection in Aerial Orthophotos

 

Fadi Dornaika¹, Abdelmalik Moujahid², Alireza Bosaghzadeh², Youssef El Merabet³, and Yassine Ruichek⁴

 

¹ University of the Basque Country (UPV/EHU) and IKERBASQUE, Basque Foundation for Science, Spain. (e-mail: fdornaika@hotmail.fr).

² University of the Basque Country (UPV/EHU), Spain. (e-mail: jibmomoa@gmail.com, alireza.bossaghzadeh@gmail.com).

³ Université Ibn Tofail, Kenitra, Morocco. (e-mail: elmerabet113@gmail.com).

⁴ IRTES-SeT, UTBM, Belfort, France. (e-mail: yassine.ruichek@utbm.fr).

 

Manuscript received on May 10, 2015,
Accepted for publication on June 5, 2015,
Published on June 15, 2015.

 

Abstract

We present a framework for automatic and accurate multiple detection of objects of interest from images using hybrid image descriptors. The proposed framework combines a powerful segmentation algorithm with a hybrid descriptor. The hybrid descriptor is composed by color histograms and several Local Binary Patterns based descriptors. The proposed framework involves two main steps. The first one consists in segmenting the image into homogeneous regions. In the second step, in order to separate the objects of interest and the image background, the hybrid descriptor of each region is classiied using machine learning tools and a gallery of training descriptors. To show its performance, the method is applied to extract building roofs from orthophotos. We provide evaluation performances over 100 buildings. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. We also show that the use of hybrid descriptors lead to an enhanced performance.

Key words: Automatic building detection and delineation, classification, supervised learning, image descriptors, orthophoto.

 

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