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Investigating the potential of deep neural networks for large-scale classification of very high resolution satellite images

Tristan Postadjian 1 A. Le Bris 1 Hichem Sahbi 2 Clément Mallet 1
1 MATIS - Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution
LaSTIG - Laboratoire des Sciences et Technologies de l'Information Géographique
2 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely buildings, roads, water, crops, vegetated areas) by exploiting existing VHR land-cover maps for training.
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https://hal.inria.fr/hal-02384451
Contributor : Clement Mallet <>
Submitted on : Thursday, November 28, 2019 - 1:38:38 PM
Last modification on : Friday, January 8, 2021 - 5:34:10 PM

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Tristan Postadjian, A. Le Bris, Hichem Sahbi, Clément Mallet. Investigating the potential of deep neural networks for large-scale classification of very high resolution satellite images. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, IV-1/W1, pp.183-190. ⟨10.5194/isprs-annals-IV-1-W1-183-2017⟩. ⟨hal-02384451⟩

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