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Can SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion / Olivier Stocker in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 557 - 564 Titre : Can SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion Type de document : Article/Communication Auteurs : Olivier Stocker, Auteur ; Arnaud Le Bris , Auteur Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France), Auteur Année de publication : 2020 Projets : MAESTRIA / Mallet, Clément Article en page(s) : pp 557 - 564 Note générale : bibliographie This work was done within the PARCELLE project funded by the French CNES TOSCA program and within the MAESTRIA project supported by the French National Research Agency under the grant ANR-18-CE23-0023. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image SPOT 7 [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) Needs for fine-grained, accurate and up-to-date land cover (LC) data are important to answer both societal and scientific purposes. Several automatic products have already been proposed, but are mostly generated out of satellite sensors like Sentinel-2 (S2) or Landsat. Metric sensors, e.g. SPOT-6/7, have been less considered, while they enable (at least annual) acquisitions at country scale and can now be efficiently processed thanks to deep learning (DL) approaches. This study thus aimed at assessing whether such sensor can improve such land cover products. A custom simple yet effective U-net - Deconv-Net inspired DL architecture is developed and applied to SPOT-6/7 and S2 for different LC nomenclatures, aiming at comparing the relevance of their spatial/spectral configurations and investigating their complementarity. The proposed DL architecture is then extended to data fusion and applied to previous sensors. At the end, the proposed fusion framework is used to enrich an existing S2 based LC product, as it is generic enough to cope with fusion at distinct levels. Numéro de notice : A2020-504 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-557-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-557-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95644 [article]

Improved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 7 (July 2020) . - pp 431 - 441 Titre : Improved crop classification with rotation knowledge using Sentinel-1 and -2 time series Type de document : Article/Communication Auteurs : Sébastien Giordano , Auteur ; Simon Bailly, Auteur ; Loïc Landrieu , Auteur ; Nesrine Chehata , Auteur Année de publication : 2020 Projets : MAESTRIA / Mallet, Clément Article en page(s) : pp 431 - 441 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] Alpes-de-haute-provence (04) [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] parcelle agricole [Termes descripteurs IGN] photo-identification [Termes descripteurs IGN] Seine-et-Marne (77) [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surface cultivée Résumé : (Auteur) Leveraging the recent availability of accurate, frequent, and multimodal (radar and optical) Sentinel-1 and -2 acquisitions, this paper investigates the automation of land parcel identification system (LPIS) crop type classification. Our approach allows for the automatic integration of temporal knowledge, i.e., crop rotations using existing parcel-based land cover databases and multi-modal Sentinel-1 and -2 time series. The temporal evolution of crop types was modeled with a linear-chain conditional random field, trained with time series of multimodal (radar and optical) satellite acquisitions and associated LPIS. Our model was tested on two study areas in France (≥ 1250 km2) which show different crop types, various parcel sizes, and agricultural practices: . the Seine et Marne and the Alpes de Haute-Provence classified accordingly to a fine national 25-class nomenclature. We first trained a Random Forest classifier without temporal structure to achieve 89.0% overall accuracy in Seine et Marne (10 classes) and 73% in Alpes de Haute-Provence (14 classes). We then demonstrated experimentally that taking into account the temporal structure of crop rotation with our model resulted in an increase of 3% to +5% in accuracy. This increase was especially important (+12%) for classes which were poorly classified without using the temporal structure. A stark positive impact was also demonstrated on permanent crops, while it was fairly limited or even detrimental for annual crops. Numéro de notice : A2020-382 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.7.431 date de publication en ligne : 01/07/2020 En ligne : https://doi.org/10.14358/PERS.86.7.431 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95428 [article]

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Current challenges in operational very high resolution land-cover mapping / Clément Mallet (2020)  

Public Titre : Current challenges in operational very high resolution land-cover mapping Type de document : Article/Communication Auteurs : Clément Mallet , Auteur ; Arnaud Le Bris , Auteur Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France), Commanditaire Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. XLIII-B2 Projets : MAESTRIA / Mallet, Clément Importance : pp 703 - 710 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] image à très haute résolution Résumé : (auteur) Many land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are not sufficient for designing better products, adapted to real-case applications, operational constraints, and the generation of services, built upon these core layers. In this paper, we review the main requirements and the recent changes in remote sensing for the specific case of very high resolution land-cover mapping. We also comment current and evaluate challenges for the optimal exploitation of Earth Observation images with the aim of automatically generating maps tailored to specific end-users’ needs. We advocate for more challenging large-scale benchmarks and for human-in-the-loop solutions. Numéro de notice : C2020-007 Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2020-703-2020 date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-703-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95657