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scientifique

GIS-based multi criteria decision making method to identify potential runoff storage zones within watershed / Vikas Kumar Rana in Annals of GIS, vol 26 n° 2 (April 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 2 (April 2020) . - pp 149 - 168 Titre : GIS-based multi criteria decision making method to identify potential runoff storage zones within watershed Type de document : Article/Communication Auteurs : Vikas Kumar Rana, Auteur ; Tallavajhala Maruthi Venkata Suryanarayana, Auteur Année de publication : 2020 Article en page(s) : pp 149 - 168 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] aide à la décision [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] bassin hydrographique [Termes descripteurs IGN] eau de surface [Termes descripteurs IGN] hydrologie [Termes descripteurs IGN] Inde [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] ruissellement [Termes descripteurs IGN] stockage [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] zone d'intérêt Résumé : (auteur) The objective of this study was to identify potential runoff storage zones based on the various physical characteristics of the Vishwamitri watershed using a GIS-based conceptual framework that combines through analytic hierarchy process using multi criteria decision-making method. The conceptual framework will help to identify potential runoff storage zones for water storage sites based on the various physical characteristics (rainfall, slope, land use/land cover, height above the nearest drainage, stream order, curve number, topographic wetness index) of the watershed. It was found out that 17% of the area is optimally suitable, 33.2% of the area is moderately suitable, 33.1% of the area is marginally suitable and 18.7% of the area is not suitable for water storage zones/structures. Results will help concerned authorities in the proficient arrangement and execution of water-related plans and schemes, improve water shortage, reduce dependability on ground water and ensure sustainable water availability for local and agricultural purposes in the study area. Numéro de notice : A2020-321 Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1733083 date de publication en ligne : 29/02/2020 En ligne : https://doi.org/10.1080/19475683.2020.1733083 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95188 [article]

Context-aware convolutional neural network for object detection in VHR remote sensing imagery / Yiping Gong in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 34 - 44 Titre : Context-aware convolutional neural network for object detection in VHR remote sensing imagery Type de document : Article/Communication Auteurs : Yiping Gong, Auteur ; Zhifeng Xiao, Auteur ; Xiaowei Tan, Auteur Année de publication : 2020 Article en page(s) : pp 34 - 44 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse contextuelle [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] description multiniveau [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] zone d'intérêt Résumé : (auteur) Object detection in very-high-resolution (VHR) remote sensing imagery remains a challenge. Environmental factors, such as illumination intensity and weather, reduce image quality, resulting in poor feature representation and limited detection accuracy. To enrich the feature representation and mine the underlying context information among objects, this article proposes a context-aware convolutional neural network (CA-CNN) model for object detection that includes proposal generation, context feature extraction, feature fusion, and classification. During feature extraction, we propose integrating a context-regions-of-interests (Context-RoIs) mining layer into the CNN model and extracting context features by mapping Context-RoIs mined from the foreground proposals to multilevel feature maps. Finally, the context features extracted from multilevel layers are fused into a single layer, and the proposals represented by the fused features are classified by a softmax classifier. In this article, through numerous experiments, we thoroughly explore the influence of key factors, such as Context-RoIs, different feature scales, and different spatial context window sizes. Because of the end-to-end network design approach, our proposed model simultaneously maintains high efficiency and effectiveness. We conducted all model testing on the public NWPU VHR-10 data set. The experimental results demonstrate that our proposed CA-CNN model achieves significantly improved model performance and better detection results compared with the state-of-the-art methods. Numéro de notice : A2020-038 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2930246 date de publication en ligne : 23/09/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2930246 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94492 [article]

Very high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)  

Public contenu dans ArXiv / Université Cornell (Etats-Unis) (1991)   Titre : Very high resolution land cover mapping of urban areas at global scale with convolutional neural network Type de document : Article/Communication Auteurs : Thomas Tilak, Auteur ; Arnaud Braun, Auteur ; David Chandler, Auteur ; Nicolas David , Auteur ; Sylvain Galopin, Auteur ; Mickaël Michaud, Auteur ; Camille Parisel, Auteur ; Matthieu Porte, Auteur ; Marjorie Robert, Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2020 Importance : 8 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] BD Alti [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] corrélation croisée maximale [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] Gironde (33) [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] vectorisation [Termes descripteurs IGN] zone d'intérêt [Termes descripteurs IGN] zone urbaine Résumé : (auteur) This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. The objective is to make a segmentation map of a large area (a french department) with the following classes: asphalt, bare soil, building, grassland, mineral material (permeable artificialized areas), forest and water from 20cm aerial images and Digital Height Model. We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class. A comparative study of different encoder-decoder architectures (U-Net, U-Net with Resnet encoders, Deeplab v3+) is presented with different loss functions. The final product is a highly valuable land cover map computed from model predictions stitched together, binarized, and refined before vectorization. Numéro de notice : P2020-006 Thématique : IMAGERIE Nature : Article nature-HAL : Préprint En ligne : https://arxiv.org/pdf/2005.05652.pdf Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95079

Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery / Ruchan Dong in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8534 - 8545 Titre : Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery Type de document : Article/Communication Auteurs : Ruchan Dong, Auteur ; Dazhuan Xu, Auteur ; Jin Zhao, Auteur ; Licheng Jiao, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 8534 - 8545 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] régression [Termes descripteurs IGN] zone d'intérêt Résumé : (auteur) Small target detection is a challenging task in veryhigh-resolution (VHR) optical remote sensing imagery, because small targets occupy a minuscule number of pixels and are easily disturbed by backgrounds or occluded by others. Although current convolutional neural network (CNN)-based approaches perform well when detecting normal objects, they are barely suitable for detecting small ones. Two practical problems stand in their way. First, current CNN-based approaches are not specifically designed for the minuscule size of small targets (~15 or ~10 pixels in extent). Second, no well-established data sets include labeled small targets and establishing one from scratch is labor-intensive and time-consuming. To address these two issues, we propose an approach that combines Sig-NMS-based Faster R-CNN with transfer learning. Sig-NMS replaces traditional non-maximum suppression (NMS) in the stage of region proposal network and decreases the possibility of missing small targets. Transfer learning can effectively label remote sensing images by automatically annotating both object classes and object locations. We conduct an experiment on three data sets of VHR optical remote sensing images, RSOD, LEVIR, and NWPU VHR-10, to validate our approach. The results demonstrate that the proposed approach can effectively detect small targets in the VHR optical remote sensing images of about 10 × 10 pixels and automatically label small targets as well. In addition, our method presents better mean average precisions than other state-of-the-art methods: 1.5% higher when performing on the RSOD data set, 17.8% higher on the LEVIR data set, and 3.8% higher on NWPU VHR-10. Numéro de notice : A2019-595 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2921396 date de publication en ligne : 15/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2921396 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94587 [article]

A 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)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 80 (August 2019) . - pp 127-136 Titre : A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm Type de document : Article/Communication Auteurs : Ana Claudia Dos Santos Luciano, Auteur ; Michelle Cristina Araújo Picoli, Auteur ; Jansle Vieira Rocha, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 127-136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] carte agricole [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] extraction de données [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] production agricole [Termes descripteurs IGN] Saccharum officinarum [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surface cultivée [Termes descripteurs IGN] zone d'intérêt Résumé : (auteur) The monitoring of sugarcane areas is important for sustainable planning and management of the sugarcane industry in Brazil. We developed an operational Object-Based Image Analysis (OBIA) classification scheme, with generalized space-time classifier, for mapping sugarcane areas at the regional scale in São Paulo State (SP). Binary random forest (RF) classification models were calibrated using multi-temporal data from Landsat images, at 10 sites located across SP. Space and time generalization were tested and compared for three approaches: a local calibration and application; a cross-site spatial generalization test with the RF model calibrated on a site and applied on other sites; and a unique space–time classifier calibrated with all sites together on years 2009–2014 and applied to the entire SP region on 2015. The local RF models Dice Coefficient (DC) accuracies at sites 1 to 8 were between 0.83 and 0.92 with an average of 0.89. The cross-site classification accuracy showed an average DC of 0.85, and the unique RF model had a DC of 0.89 when compared with a reference map of 2015. The results demonstrated a good relationship between sugarcane prediction and the reference map for each municipality in SP, with R² = 0.99 and only 5.8% error for the total sugarcane area in SP, and compared with the area inventory from the Brazilian Institute of Geography and Statistics, with R² = 0.95 and –1% error for the total sugarcane area in SP. The final unique RF model allowed monitoring sugarcane plantations at the regional scale on independent year, with efficiency, low-cost, limited resources and a precision approximating that of a photointerpretation. Numéro de notice : A2019-470 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.04.013 date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.1016/j.jag.2019.04.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93612 [article]

Segmentation d'image par intégration itérative de connaissances / Mahaman Sani Chaibou Salaou (2019)  

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An efficient technique for creating a continuum of equal-area map projections / Daniel "daan" Strebe in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

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Caractérisation et qualification de Modèles Numériques de Surfaces (MNS) - Analyse de la cohérence avec des masques d’eau / Guillaume Sutter (2018)

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Machine learning and pose estimation for autonomous robot grasping with collaborative robots / Victor Talbot (2018) 

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The geometry of space-time prisms with uncertain anchors / Bart Kuijpers in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)  

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Local Moebius transformations applied to omnidirectional images / Leonardo Souto Ferreira in Computers and graphics, vol 68 (November 2017)  

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An adaptable equal-area pseudoconic map projection / Daniel "daan" Strebe in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)  

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A region-line primitive association framework for object-based remote sensing image analysis / Wang Min in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)

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Seamline determination for high resolution orthoimage mosaicking using watershed segmentation / Wang Mi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)

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Multiangle BSAR imaging based on BeiDou-2 navigation satellite system: experiments and preliminary results / Tao Zeng in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

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