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Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6039 - 6058 Titre : Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis Type de document : Article/Communication Auteurs : David Solarna, Auteur ; Alberto Gotelli, Auteur ; Jacqueline Le Moigne, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6039 - 6058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] cratère [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] distance de Hausdorff [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] Mars (planète) [Termes descripteurs IGN] ondelette [Termes descripteurs IGN] processus ponctuel marqué [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] transformation de Hough [Termes descripteurs IGN] zone d'intérêt Résumé : (auteur) Because of the large variety of planetary sensors and spacecraft already collecting data and with many new and improved sensors being planned for future missions, planetary science needs to integrate numerous multimodal image sources, and, as a consequence, accurate and robust registration algorithms are required. In this article, we develop a new framework for crater detection based on marked point processes (MPPs) that can be used for planetary image registration. MPPs were found to be effective for various object detection tasks in Earth observation, and a new MPP model is proposed here for detecting craters in planetary data. The resulting spatial features are exploited for registration, together with fitness functions based on the MPP energy, on the mean directed Hausdorff distance, and on the mutual information. Two different methods—one based on birth–death processes and region-of-interest analysis and the other based on graph cuts and decimated wavelets—are developed within the proposed framework. Experiments with a large set of images, including 13 thermal infrared and visible images of the Mars surface, 20 semisimulated multitemporal pairs of images of the Mars surface, and a real multitemporal image pair of the Lunar surface, demonstrate the effectiveness of the proposed framework in terms of crater detection performance as well as for subpixel registration accuracy. Numéro de notice : A2020-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2970908 date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2970908 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95704 [article]

A novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 190 - 200 Titre : A novel deep learning instance segmentation model for automated marine oil spill detection Type de document : Article/Communication Auteurs : Shamsudeen Temitope Yekeen, Auteur ; Abdul‐Lateef Balogun, Auteur ; Khamaruzaman B. Wan Yusof, Auteur Année de publication : 2020 Article en page(s) : pp 190 - 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] hydrocarbure [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] marée noire [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] zone d'intérêt Résumé : (auteur) The visual similarity of oil slick and other elements, known as look-alike, affects the reliability of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and discrimination of oil spill and look-alike are still limited to the use of traditional machine learning algorithms and semantic segmentation deep learning models with limited accuracy. Thus, this study developed a novel deep learning oil spill detection model using computer vision instance segmentation Mask-Region-based Convolutional Neural Network (Mask R-CNN) model. The model training was conducted using transfer learning on the ResNet 101 on COCO as backbone in combination with Feature Pyramid Network (FPN) architecture for feature extraction at 30 epochs with 0.001 learning rate. Testing of the model was conducted using the least training and validation loss value on the withheld testing images. The model’s performance was evaluated using precision, recall, specificity, IoU, F1-measure and overall accuracy values. Ship detection and segmentation had the highest performance with overall accuracy of 98.3%. The model equally showed a higher accuracy for oil spill and look-alike detection and segmentation although oil spill detection outperformed look-alike with overall accuracy values of 96.6% and 91.0% respectively. The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation. Numéro de notice : A2020-548 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.011 date de publication en ligne : 28/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95774 [article]

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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 Affiliation des auteurs : non IGN 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 Affiliation des auteurs : non IGN 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 Projets : 1-Pas de projet / 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] chaîne de production [Termes descripteurs IGN] chaîne de traitement [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 Affiliation des auteurs : IGN+Ext (avant 2012) Thématique : IMAGERIE Nature : Article nature-HAL : Préprint DOI : sans 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)  

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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)  

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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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