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Recherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois

Abstract : Abstract This paper introduces a multi-modal network that learns to match aerial images of french urban and rural territories taken about 15 years apart. This means it should be invariant against a big range of changes as the (natural) landscape evolves over time. It leverages the original images and semantically segmented and labeled regions. The core of the method is a siamese network that learns to extract features from corresponding image pairs across time, and non matching pairs. These descriptors are discri-minative enough, such that a simple kNN classifier on top, suffices as a final geo-matching criteria. We demonstrate that our siamese descriptor outperforms other image descriptors for cross-time image retrieval.
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Contributor : Margarita Khokhlova Connect in order to contact the contributor
Submitted on : Wednesday, July 29, 2020 - 3:10:04 PM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM
Long-term archiving on: : Tuesday, December 1, 2020 - 6:39:31 PM


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  • HAL Id : hal-02906569, version 1


Margarita Khokhlova, Valérie Gouet-Brunet, Nathalie Abadie, Liming Chen. Recherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois. Conférence française en Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP 2020),, Jun 2020, Vannes, France. ⟨hal-02906569⟩



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