Descripteur
Documents disponibles dans cette catégorie (340)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])
[article]
Titre : Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Fumin Wang, Auteur ; Jingfeng Huang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1088 - 1108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] jeu de données
[Termes IGN] polarisation
[Termes IGN] rizière
[Termes IGN] surface cultivéeRésumé : (auteur) SVM and RF are widely used in rice mapping. However, their performance with single and different combinations of satellite datasets is rarely reported. Hence we report their rice mapping accuracies for two seasons using Sentinel-1A, Landsat-8 and Sentinel-2A images. The VH and VV polarizations of Sentinel-1A, and two spectral indices (SIs) of Landsat-8 and Sentine1-2A were used to obtain seven datasets (VH, VV, SI, VHVV, VHSI, VVSI and VHVVSI), and on which SVM and RF were applied and accuracies were assessed. VHSI showed the highest overall accuracy for both algorithms in both years. SVM with VHSI had a slightly higher accuracy (90.8%) than RF with VHSI (89.2%) in 2015 while in 2016 RF with VHSI showed a slightly higher accuracy (95.2%) than SVM with VHSI (93.4%). Although they produced equivalent accuracies within years, RF is more sensitive to additional data, given a 6.0% increase from 2015 to 2016 with VHSI. Numéro de notice : A2020-443 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1568586 Date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1568586 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95501
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1088 - 1108[article]Detecting abandoned farmland using harmonic analysis and machine learning / Heeyeun Yoon in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
[article]
Titre : Detecting abandoned farmland using harmonic analysis and machine learning Type de document : Article/Communication Auteurs : Heeyeun Yoon, Auteur ; Soyoun Kim, Auteur Année de publication : 2020 Article en page(s) : pp 201 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse harmonique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Corée du sud
[Termes IGN] gestion des ressources
[Termes IGN] inventaire
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Normalized Difference Water Index
[Termes IGN] phénologie
[Termes IGN] production agricole
[Termes IGN] Soil Adjusted Vegetation Index
[Termes IGN] surface cultivéeRésumé : (auteur) It is critical to inventory abandoned farmland soon after it is generated, to better manage agricultural resources and to prevent negative consequences that would otherwise follow. This study aims to distinguish abandoned farmlands from active croplands—rice paddy and agricultural fields—by discerning the phenological trajectories over a short-term period of three years (Jan. 2016 to Dec. 2018) in Gwanyang City in South Korea. For Support Vector Machine (SVM) classification, we fully utilized parameters derived from harmonic analyses of the three vegetation indices (VIs: NDVI, NDWI, and SAVI) extracted from Sentinel-2A imagery. The harmonic analyses proved that higher-order sinusoid components produced better fitting to explain the trajectory of the VIs—the maximum adjusted was 95.23%—and the multiple VIs diversified the attributes for the classifications. Consequently, the higher-order harmonic components and the additional VIs increased the accuracy when used in SVM classification. The best performing classification was achieved with a composite of harmonic terms derived from the three VIs, yielding overall accuracy of 90.72%, Kappa index of 0.858, and user’s accuracy for abandoned farmland of 93.40%. The proposed method here would greatly improve the process of detecting abandoned farmland, despite a relatively short observation period, and enable a rapid response to the occurrence of abandonment. Numéro de notice : A2020-356 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.021 Date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95243
in ISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 201 - 212[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Small‐area patch‐merging method accounting for both local constraints and the overall area balance / Chengming Li in Transactions in GIS, Vol 24 n° 4 (August 2020)
[article]
Titre : Small‐area patch‐merging method accounting for both local constraints and the overall area balance Type de document : Article/Communication Auteurs : Chengming Li, Auteur ; Yong Yin, Auteur ; Zhaoxin Dai, Auteur Année de publication : 2020 Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] contrainte relationnelle
[Termes IGN] fusion (généralisation)
[Termes IGN] itération
[Termes IGN] Kouangtoung (Chine)
[Termes IGN] parcelle agricole
[Termes IGN] structure hiérarchique de données
[Termes IGN] superficie
[Termes IGN] Triangulated Irregular Network
[Termes IGN] triangulation de Delaunay
[Termes IGN] utilisation du sol
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Small‐area patch merging is a common operation in land use data generalization. However, existing research on small‐area patch merging has mainly focused on local compatibility measures, which often lead to area imbalances among land use types from a global perspective. To address the shortcomings of previous studies by resolving local and global concerns simultaneously, this article proposes a merging method that considers both local constraints and the overall area balance. First, a local optimization model that considers three constraints—namely, the areas of neighboring patches, the lengths of shared arcs, and semantic similarity—is established. The areas of small patches are first pre‐allocated. Subsequently, in accordance with an area change threshold for individual land use types, land use types with area changes that exceed this threshold are identified. The patches corresponding to these land use types are subjected to iterative adjustments while considering the overall area balance. Based on their area splitting abilities, the split lines for small‐area patches are determined, and small‐area patches are merged. Finally, actual data from Guangdong Province are used for validation. The experimental results demonstrate that the proposed method is capable of preserving the local compatibility of patches while balancing the overall area associated with each land use type. Numéro de notice : A2020-672 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12634 Date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1111/tgis.12634 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96157
in Transactions in GIS > Vol 24 n° 4 (August 2020) . - 21 p.[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)
[article]
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 IGN] Alpes-de-haute-provence (04)
[Termes IGN] chaîne de traitement
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] parcelle agricole
[Termes IGN] photo-identification
[Termes IGN] Seine-et-Marne (77)
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (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 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) 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
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 7 (July 2020) . - pp 431 - 441[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2020071 SL Revue Centre de documentation Revues en salle Disponible An integrated approach for detection and prediction of greening situation in a typical desert area in China and its human and climatic factors analysis / Lei Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)
[article]
Titre : An integrated approach for detection and prediction of greening situation in a typical desert area in China and its human and climatic factors analysis Type de document : Article/Communication Auteurs : Lei Zhou, Auteur ; Siyu Wang, Auteur ; Mingyi Du, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bassin hydrographique
[Termes IGN] changement climatique
[Termes IGN] changement d'utilisation du sol
[Termes IGN] Chine
[Termes IGN] couvert végétal
[Termes IGN] désert
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] image Landsat
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle de simulation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) The combined study of vegetation coverage (VC) and land use change provides important scientific guidance for the restoration and protection of arid regions. Taking Hongjian Nur (HJN) Lake in the desert region as a case study, the VC of this area was calculated using a normalized difference vegetation index (NDVI), which is based on a mixed pixel decomposition method. A grey forecasting model (GM) (1, 1) was used to predict future VC. The driving factors of VC and land use change were analyzed. The results indicate that the average VC of the whole watershed showed a gradual increase from 0.29 to 0.49 during 2000–2017. The prediction results of the GM VC showed that the greening trend is projected to continue until 2027. The area of farmland in the watershed increased significantly and its area was mainly converted from unused land, grassland, and forest. The reason for increased VC may be that the combination of the exploitation of unused land and climate change, which is contrary to the country’s sustainable development goals (SDG; goal 15). Therefore, the particularities of the local ecological environment in China’s desert area needs to be considered in the development of ecological engineering projects. Numéro de notice : A2020-311 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060364 Date de publication en ligne : 02/06/2020 En ligne : https://doi.org/10.3390/ijgi9060364 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95163
in ISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 24 p.[article]Les missions photogrammétriques réalisées par drone au centimètre sans points de calage au sol / Olivier Degueldre in XYZ, n° 162 (mars 2020)PermalinkA convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkEstimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkCombination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan / Emal Wali in Remote sensing, vol 12 n° 1 (January 2020)PermalinkPermalinkSatellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)PermalinkPermalinkCombining 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)PermalinkComparative 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)PermalinkMultitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador / Nguyen-Thanh Son in Geocarto international, vol 34 n° 12 ([15/09/2019])PermalinkExploring 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)PermalinkPermalinkMonitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations / Abdelhakim Amazirh (2019)PermalinkPermalinkLand parcel management system in Poland and a case study of EU member states / Agnieszka Trystuła in Geodetski vestnik, vol 62 n° 4 (December 2018 - February 2019)PermalinkSeparating the influence of vegetation changes in polarimetric differential SAR interferometry / Virginia Brancato in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkObject-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)PermalinkMapping rubber trees based on phenological analysis of Landsat time series data-sets / Janatul Aziera binti Abd Razak in Geocarto international, vol 33 n° 6 (June 2018)PermalinkImproving the upscaling of land cover maps by fusing uncertainty and spatial structure information / Peijun Sun in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 2 (February 2018)PermalinkConception d’une méthode radar de suivi bimensuel des déforestations et d’une méthode optique de classification d’occupation des sols / Luc Baudoux (2018)PermalinkEstimation of surface roughness over bare agricultural soil from Sentinel-1 data / Mohammad Choker (2018)PermalinkPermalinkDeveloping detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor / Abel Chemura in Geocarto international, vol 32 n° 7 (July 2017)PermalinkFusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area / Mohamed Barakat A. Gibril in Geocarto international, vol 32 n° 7 (July 2017)PermalinkViCTOr : paysage virtuel pour explorer les dynamiques de la VIticulture et de la Consommation en TouRaine / Etienne Delay in Cybergeo, European journal of geography, n° 2017 ([01/06/2017])PermalinkAgricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests / M.F.A. Vogels in International journal of applied Earth observation and geoinformation, vol 54 (February 2017)PermalinkAnalyse de séries temporelles d’images Sentinel et intégration de connaissances pour la classification en milieu agricole / Simon Bailly (2017)PermalinkLe commentaire de carte topographique / Camille Tiano (2017)PermalinkContributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar / Nesrine Chehata (2017)PermalinkSols artificialisés et processus d’artificialisation des sols : déterminants, impacts et leviers d’action / Béatrice Béchet (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)PermalinkCHP toolkit : case study of LAIe sensitivity to discontinuity of canopy cover in fruit plantations / Karolina D. Fieber in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkUse of SAR data for detecting floodwater in urban and agricultural areas: the role of the interferometric coherence / Luca Pulvirenti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkPermalinkAutomated annual cropland mapping using knowledge-based temporal features / François Waldner in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)PermalinkExtraction des zones cohérentes par l’analyse spatio-temporelle d’images de télédétection / Thomas Guyet in Revue internationale de géomatique, vol 25 n° 4 (octobre - décembre 2015)PermalinkUtilisation des technologies géospatiales pour l'évaluation des transformations spatiales dues aux pressions anthropiques dans le canton Afféma (Sud-est ivoirien) / Armand Kangah in Photo interprétation, European journal of applied remote sensing, vol 51 n° 3 (septembre 2015)Permalink