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Satellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)
Titre : Satellite image time series classification with pixel-set encoders and temporal self-attention Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu , Auteur ; Sébastien Giordano , Auteur ; Nesrine Chehata , Auteur Editeur : Computer vision foundation CVF Année de publication : 2020 Projets : 1-Pas de projet / Conférence : CVPR 2020, IEEE Conference on Computer Vision and Pattern Recognition 14/06/2020 19/06/2020 en ligne Chine Open Access Proceedings Importance : pp 12325 - 12334 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification automatique
[Termes IGN] classification orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] géocodage
[Termes IGN] image multibande
[Termes IGN] image satellite
[Termes IGN] parcelle agricole
[Termes IGN] politique agricole commune
[Termes IGN] série temporelle
[Termes IGN] utilisation du solRésumé : (auteur) Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions. In particular, large-scale control of agricultural parcels is an issue of major political and economic importance. In this regard, hybrid convolutional-recurrent neural architectures have shown promising results for the automated classification of satellite image time series.We propose an alternative approach in which the convolutional layers are advantageously replaced with encoders operating on unordered sets of pixels to exploit the typically coarse resolution of publicly available satellite images. We also propose to extract temporal features using a bespoke neural architecture based on self-attention instead of recurrent networks. We demonstrate experimentally that our method not only outperforms previous state-of-the-art approaches in terms of precision, but also significantly decreases processing time and memory requirements. Lastly, we release a large openaccess annotated dataset as a benchmark for future work on satellite image time series. Numéro de notice : C2020-016 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv/vers CVF Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/CVPR42600.2020.01234 Date de publication en ligne : 05/08/2020 En ligne : https://doi.org/10.1109/CVPR42600.2020.01234 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94225 Documents numériques
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Satellite image time series classification - pdf préprintAdobe Acrobat PDF
Titre : Sustainable land management in a European context : A co-design approach Type de document : Monographie Auteurs : Thomas Weith, Éditeur scientifique ; Tim Barkmann, Auteur ; Nadin Gaasch, Éditeur scientifique ; et al., Éditeur scientifique Editeur : Springer Nature Année de publication : 2020 Importance : 347 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-50841-8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Ecologie
[Termes IGN] aménagement durable
[Termes IGN] aménagement rural
[Termes IGN] changement d'utilisation du sol
[Termes IGN] écosystème
[Termes IGN] Europe (géographie politique)
[Termes IGN] géosciences
[Termes IGN] gestion foncière
[Termes IGN] gestion forestière durable
[Termes IGN] paysage urbain
[Termes IGN] politique communautaire
[Termes IGN] recherche interdisciplinaire
[Termes IGN] sciences de l'environnement
[Termes IGN] surface cultivée
[Termes IGN] surveillance de l'urbanisationRésumé : (éditeur) This open access book presents and discusses current issues and innovative solution approaches for land management in a European context. Manifold sustainability issues are closely interconnected with land use practices. Throughout the world, we face increasing conflict over the use of land as well as competition for land. Drawing on experience in sustainable land management gained from seven years of the FONA programme (Research for Sustainable Development, conducted under the auspices of the German Federal Ministry of Education and Research), the book stresses and highlights co-design processes within the "co-creation of knowledge", involving collaboration in transdisciplinary research processes between academia and other stakeholders. The book begins with an overview of the current state of land use practices and the subsequent need to manage land resources more sustainably. New system solutions and governance approaches in sustainable land management are presented from a European perspective on land use. The volume also addresses how to use new modes of knowledge transfer between science and practice. New perspectives in sustainable land management and methods of combining knowledge and action are presented to a broad readership in land system sciences and environmental sciences, social sciences and geosciences. Note de contenu : 1- Land-use: State and drivers in Europe
2- Co-production of knowledge
3- Co-evolution: New system solutions and governance
4- OutlookNuméro de notice : 25987 Affiliation des auteurs : non IGN Thématique : FORET Nature : Recueil / ouvrage collectif DOI : 10.1007/978-3-030-50841-8 En ligne : https://doi.org/10.1007/978-3-030-50841-8 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96745 Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (October-2 2019)
[article]
Titre : Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data Type de document : Article/Communication Auteurs : Emanuele Santi, Auteur ; Mohammed Dabboor, Auteur ; Simone Pettinato, Auteur ; Simonetta Paloscia, Auteur Année de publication : 2019 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] bande C
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] Manitoba (Canada)
[Termes IGN] polarimétrie
[Termes IGN] polarisation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (O°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than O°, with correlation coe?cients up to R ' 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of O° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized O°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9. Numéro de notice : A2019-555 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11202451 Date de publication en ligne : 22/10/2019 En ligne : https://doi.org/10.3390/rs11202451 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94210
in Remote sensing > Vol 11 n° 20 (October-2 2019) . - 18 p.[article]Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands / Mohammad El Hajj in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)
[article]
Titre : Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands Type de document : Article/Communication Auteurs : Mohammad El Hajj, Auteur ; Nicolas Baghdadi, Auteur ; Mehrez Zribi, Auteur Année de publication : 2019 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] humidité du sol
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Water Index
[Termes IGN] réseau neuronal artificiel
[Termes IGN] surface cultivéeRésumé : (auteur) Surface soil moisture (SSM) estimation is of great importance in several areas, such as hydrology, agriculture and risk assessment. C-band SAR (synthetic aperture radar) data have been widely used to estimate SSM, whereas few studies have been performed using L-band SAR due to the low availability of L-band SAR data. In this context, the objective of the present paper is to compare the SSM estimation potentials of the C- (Sentinel-1) and L-bands (PALSAR) for wheat and grassland plots. The inversion approach developed in this study uses neural networks to invert the SAR signal and estimate the SSM. For each radar frequency, the developed neural networks were trained using the following as an input vector: SAR incidence angle, SAR polarization (VV for the C-band and HH for the L-band), and NDVI from optical images. Artificial Neural networks (ANNs) were developed and validated using synthetic and real databases. The results showed that the L-band provided slightly less accurate SSM estimates than the C-band. Moreover, the results showed that the accuracies of the SSM estimates for both frequencies strongly depended on the soil roughness (Hrms) and SSM values. From the synthetic database at SSM values less than 25 vol.%, the ANNs underestimated the SSM for Hrms values less than 1.5 cm and overestimated the SSM for Hrms values greater than 1.5 cm. In addition, the ANNs underestimated the SSM value regardless of the Hrms value when the SSM value was greater than 25 vol.%. An RMSE analysis of the SSM estimates showed that the highest RMSE values were observed for the L-band regardless of the SSM value, and high RMSE values were observed for the C-band only in very wet soil conditions (SSM>25 vol.%). From the real database at NDVI values less than 0.7, the RMSE (root mean square error) of the SSM estimates was 4.6 vol.% for the C-band and 5.3 vol.% for the L-band. Most importantly, the L-band enabled the estimation of the SSM under a well-developed vegetation cover (NDVI > 0.7) with an RMSE of 6.7 vol.%, whereas the C-band SAR signal became completely attenuated for some crops when the NDVI value was greater than 0.7, and thus the estimation of SSM was impossible using the C-band. Numéro de notice : A2019-473 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.05.021 Date de publication en ligne : 29/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.05.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93634
in International journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 13 p.[article]Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador / Nguyen-Thanh Son in Geocarto international, vol 34 n° 12 ([15/09/2019])
[article]
Titre : Multitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador Type de document : Article/Communication Auteurs : Nguyen-Thanh Son, Auteur ; Chi-Farn Chen, Auteur ; Cheng-Ru Chen, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1363 - 1383 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] bande infrarouge
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] climat tropical
[Termes IGN] coefficient de corrélation
[Termes IGN] fusion de données
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Landsat
[Termes IGN] image Terra-MODIS
[Termes IGN] saison
[Termes IGN] Salvador
[Termes IGN] sécheresse
[Termes IGN] surface cultivée
[Termes IGN] température au solRésumé : (Auteur) This study aims to develop an approach to characterize cropland drought conditions in El Salvador, Central America. The data were processed for 2016–2017 through three main steps: (1) reconstructing MODIS land-surface temperature (LST), (2) Landsat-MODIS data fusion and (3) drought delineation using the temperature vegetation dryness index (TVDI). The results of LST reconstruction using the random forests (RF) indicated the median RMSE value of 0.5 °C. The fusion results achieved from the STARFM compared with the reference Landsat data revealed close agreement with the correlation coefficient (r) values higher than 0.84. The TVDI results verified with that from the reference Landsat data indicated r values of 0.85 and 0.75 for 2016 and 2017, respectively. The larger very dry area was observed for the 2016 primera season due to prolonged droughts. Approximately 11.5% and 10.7% of croplands were, respectively, associated with very dry moisture condition in the 2016 and 2017 primera seasons. Numéro de notice : A2019-466 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1489421 Date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1489421 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93619
in Geocarto international > vol 34 n° 12 [15/09/2019] . - pp 1363 - 1383[article]Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data / Alexander Cass in Applied geomatics, vol 11 n° 3 (September 2019)PermalinkA generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm / Ana Claudia Dos Santos Luciano in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)PermalinkEvaluating metrics derived from Landsat 8 OLI imagery to map crop cover / Rei Sonobe in Geocarto international, vol 34 n° 8 ([15/06/2019])PermalinkLong-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia / Enkhjargal Natsagdorj in Geocarto international, vol 34 n° 7 ([01/06/2019])PermalinkCartographie de l’aléa érosif dans le bassin sud du Litani-Liban / Hussein El Hage Hassan in Revue internationale de géomatique, vol 29 n° 2 (avril - juin 2019)PermalinkJournées de la recherche 2019 / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)PermalinkDe l'agrandissement des exploitations agricoles à la transformation des paysages de bocage : analyse comparative des recompositions foncières et paysagères en Normandie / Thibault Preux (2019)PermalinkChangements du stock de bois sur pied des forêts françaises : description, analyse et simulation sur des horizons temporels pluri-décennal (1975 - 2015) et séculaire à partir des données de l'inventaire forestier national et de statistiques anciennes / Anaïs Denardou-Tisserand (2019)PermalinkEvaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands / Fabio Castaldi in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkPermalink