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Integrating edge/boundary priors with classification scores for building detection in very high resolution data

Résumé : Automatic and accurate detection of man-made objects, such as buildings, is one of the main problems that the remote sensing community has been focusing on for the last decades. In this paper, we propose a Conditional Random Field (CRF) formulation which is using edge/boundary localization priors towards accurate building detection. These edge priors have been integrated/fused with the classification scores from a deep learning Convolutional Neural Network (CNN) architecture under a single energy formulation. The validation of the developed methodology had been performed on the recently published SpaceNet dataset. Experimental results and quantitative evaluation, based on different accuracy statistics, indicate the great potential of the proposed approach.
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Conference papers
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https://hal-centralesupelec.archives-ouvertes.fr/hal-02423036
Contributor : Kamilia Abdani <>
Submitted on : Monday, December 23, 2019 - 3:35:58 PM
Last modification on : Friday, July 31, 2020 - 10:44:11 AM

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Maria Vakalopoulou, Norbert Bus, Konstantinos Karantzalos, Nikos Paragios. Integrating edge/boundary priors with classification scores for building detection in very high resolution data. IEEE International geoscience and remote sensing symposium (IGARSS), Jul 2017, Fort Worth, TX, United States. pp.3309-3312, ⟨10.1109/IGARSS.2017.8127705⟩. ⟨hal-02423036⟩

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