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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)
[article]
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 IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] hydrocarbure
[Termes IGN] image radar moirée
[Termes IGN] marée noire
[Termes IGN] segmentation sémantique
[Termes IGN] vision par ordinateur
[Termes IGN] zone d'intérêtRésumé : (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
in ISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 190 - 200[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt A novel deep network and aggregation model for saliency detection / Ye Liang in The Visual Computer, vol 36 n° 9 (September 2020)
[article]
Titre : A novel deep network and aggregation model for saliency detection Type de document : Article/Communication Auteurs : Ye Liang, Auteur ; Hongzhe Liu, Auteur ; Nan Ma, Auteur Année de publication : 2020 Article en page(s) : pp 1883 - 1895 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] architecture de réseau
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déconvolution
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] saillanceRésumé : (auteur) Recent deep learning-based methods for saliency detection have proved the effectiveness of integrating features with different scales. They usually design various complex architectures of network, e.g., multiple networks, to explore the multi-scale information of images, which is expensive in computation and memory. Feature maps produced with different subsampling convolutional layers have different spatial resolutions; therefore, they can be used as the multi-scale features to reduce the costs. In this paper, by exploiting the in-network feature hierarchy of convolutional networks, we propose a novel multi-scale network for saliency detection (MSNSD) consisting of three modules, i.e., bottom-up feature extraction, top-down feature connection and multi-scale saliency prediction. Moreover, to further boost the performance of MSNSD, an input image-aware saliency aggregation method is proposed based on the ridge regression, which combines MSNSD with some well-performed handcrafted shallow models. Extensive experiments on several benchmarks show that the proposed MSNSD outperforms the state-of-the-art saliency methods with less computational and memory complexity. Meanwhile, our aggregation method for saliency detection is effective and efficient to combine deep and shallow models and make them complementary to each other. Numéro de notice : A2020-601 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-019-01781-9 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.1007/s00371-019-01781-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95952
in The Visual Computer > vol 36 n° 9 (September 2020) . - pp 1883 - 1895[article]Ontologie d’Alerte Choucas : de la modélisation des connaissances à un outil support d’un raisonnement géovisuel / Matthieu Viry in Geomatica, vol 74 n° 3 (September 2020)
[article]
Titre : Ontologie d’Alerte Choucas : de la modélisation des connaissances à un outil support d’un raisonnement géovisuel Type de document : Article/Communication Auteurs : Matthieu Viry, Auteur ; Marlène Villanova-Oliver, Auteur Année de publication : 2020 Projets : CHOUCAS / Olteanu-Raimond, Ana-Maria Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes IGN] interface graphique
[Termes IGN] ontologie
[Termes IGN] secours d'urgence
[Termes IGN] zone d'intérêt
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Lorsqu’une intervention de secours est nécessaire, localiser précisément et rapidement le site sur lequel envoyer les équipes est primordial. La littérature montre que des outils de géovisualisation constituent des solutions pertinentes pour supporter des processus d’analyse d’informations dans des contextes variés. Nous nous intéressons ici au raisonnement du secouriste réceptionnant un appel à l’aide et visons des solutions conceptuelles et logicielles dédiées à la tâche de détermination de la localisation de la victime, plus particulièrement dans le contexte du secours en montagne. Nous avons formalisé le raisonnement du secouriste et les informations sur lesquelles il ou elle s’appuie à l’aide d’une ontologie. L’Ontologie d’Alerte Choucas structure les concepts exploités par le secouriste qui élabore des hypothèses de localisation probable de la victime à partir d’informations (telles qu’une position relative, un temps de marche, une direction) fournies lors d’un échange verbal. Dans notre approche, l’ontologie est en outre exploitée pour dériver les composants d’interface d’un prototype de géovisualisation facilitant le raisonnement du secouriste. Ces composants sont une aide à la saisie des informations, en fournissant une restitution cartographique adaptée, et contribuent à construire et à affiner la zone de localisation. Notre approche présente une chaine de traitements originale, menant de la représentation des connaissances à la génération automatisée d’une interface fonctionnelle d’aide au raisonnement visant à localiser des victimes en montagne. Numéro de notice : A2020-840 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1139/geomat-2020-0005 En ligne : https://doi.org/10.1139/geomat-2020-0005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98319
in Geomatica > vol 74 n° 3 (September 2020)[article]Precise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
[article]
Titre : Precise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering Type de document : Article/Communication Auteurs : Ali Ozgun Ok, Auteur ; Asli Ozdarici-Ok, Auteur Année de publication : 2020 Article en page(s) : pp 557-569 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse de groupement
[Termes IGN] Citrus limon
[Termes IGN] détection de contours
[Termes IGN] état de l'art
[Termes IGN] extraction d'arbres
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle stochastique
[Termes IGN] TurquieRésumé : (Auteur) In this study, we present an original unified strategy for the precise extraction of individual citrus fruit trees from single digital surface model (DSM) input data. A probabilistic method combining the circular shape information with the knowledge of the local maxima in the DSM has been used for the detection of the candidate trees. An active contour is applied within each detected region to extract the borders of the objects. Thereafter, all extracted objects are seamlessly divided into clusters considering a new feature data set formed by (1) the properties of trees, (2) planting parameters, and (3) neighborhood relations. This original clustering stage has led to two new contributions: (1) particular objects or clustered structures having distinctive characters and relationships other than the citrus objects can be identified and eliminated, and (2) the information revealed by clustering can be used to recover missing citrus objects within and/or nearby each cluster. The main finding of this research is that a successful clustering can provide valuable input for identifying incorrect and missing information in terms of citrus tree extraction. The proposed strategy is validated in eight test sites selected from the northern part of Mersin province of Turkey. The results achieved are also compared with the state-of-the-art methods developed for tree extraction, and the success of the proposed unified strategy is clearly highlighted. Numéro de notice : A2020-491 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.9.557 Date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.14358/PERS.86.9.557 Format de la ressource électronique : LUR article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95933
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 9 (September 2020) . - pp 557-569[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2020091 SL Revue Centre de documentation Revues en salle Disponible Semi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
[article]
Titre : Semi-automatic building extraction from WorldView-2 imagery using taguchi optimization Type de document : Article/Communication Auteurs : Hasan Tonbul, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2020 Article en page(s) : pp 547-555 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de variance
[Termes IGN] carte d'occupation du sol
[Termes IGN] détection du bâti
[Termes IGN] extraction semi-automatique
[Termes IGN] image Worldview
[Termes IGN] optimisation (mathématiques)
[Termes IGN] rapport signal sur bruit
[Termes IGN] régression linéaire
[Termes IGN] segmentation multi-échelle
[Termes IGN] séparateur à vaste margeRésumé : (Auteur) Due to the complex spectral and spatial structures of remotely sensed images, the delineation of land use/land cover classes using conventional approaches is a challenging task. This article tackles the problem of seeking optimal parameters of multi-resolution segmentation for a classification task using WorldView-2 imagery. Taguchi optimization was applied to search optimal parameters using the plateau objective function (POF) and quality rate (Qr) as fitness criteria. Analysis of variance was also used to estimate the contributions of the parameters for POF and Qr, separately. The scale parameter was the most effective one, with contribution levels of 87.45% and 56.87% for POF and Qr, respectively. Linear regression and support-vector regression methods were used to predict the results of the experiment. Test results revealed that Taguchi optimization was more effective than linear regression and support-vector regression for predicting POF and Qr values. Numéro de notice : A2020-490 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.9.547 Date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.14358/PERS.86.9.547 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95931
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 9 (September 2020) . - pp 547-555[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2020091 SL Revue Centre de documentation Revues en salle Disponible Ship detection in SAR images via local contrast of Fisher vectors / Xueqian Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkA spaceborne SAR-based procedure to support the detection of landslides / Giuseppe Esposito in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)PermalinkThermal and spatial data integration for recreating rebuilding stages of wooden and masonry buildings / Paulina Lewińska in Photogrammetric record, vol 35 n° 171 (September 2020)PermalinkVehicle detection of multi-source remote sensing data using active fine-tuning network / Xin Wu in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkShoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method / Audrey Minghelli in Remote sensing, vol 12 n° 16 (August-2 2020)PermalinkSemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-5-2020 (August 2020)PermalinkBreaking the eyes: how do users get started with a coordinated and multiple view geovisualization tool? / Izabela Golebiowska in Cartographic journal (the), Vol 57 n° 3 (August 2020)PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkExtraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkGuided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkNear-real time forecasting and change detection for an open ecosystem with complex natural dynamics / Jasper A. Slingsby in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)PermalinkRecent changes in two outlet glaciers in the Antarctic Peninsula using multi-temporal Landsat and Sentinel-1 data / Carolina L. Simões in Geocarto international, vol 35 n° 11 ([01/08/2020])PermalinkStructure from motion for complex image sets / Mario Michelini in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)PermalinkClassification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkClassification of sea ice types in Sentinel-1 SAR data using convolutional neural networks / Hugo Boulze in Remote sensing, vol 12 n° 13 (July-1 2020)PermalinkEvaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches / S.M. Hamylton in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)PermalinkA novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images / David Pirrone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkSemi-automatic identification of submarine pipelines with synthetic aperture sonar Images / Victor Hugo Fernandes in Marine geodesy, Vol 43 n° 4 (July 2020)PermalinkSimulating urban land use change by integrating a convolutional neural network with vector-based cellular automata / Yaqian Zhai in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)PermalinkSubpixel-pixel-superpixel-based multiview active learning for hyperspectral images classification / Yu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkAn Illumination Insensitive descriptor combining the CSLBP features for street view images in augmented reality: experimental studies / Zejun Xiang in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkAn 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)PermalinkCounting of grapevine berries in images via semantic segmentation using convolutional neural networks / Laura Zabawa in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkEnsemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkFine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data / Shivangi Srivastava in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)PermalinkGeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning / Samantha T. Arundel in Transactions in GIS, Vol 24 n° 3 (June 2020)PermalinkHyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance / Bing Tu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkIndoor positioning using PnP problem on mobile phone images / Hana Kubickova in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkObject-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery / Zhenhui Sun in Geocarto international, Vol 35 n° 8 ([01/06/2020])PermalinkPhotogrammetric determination of 3D crack opening vectors from 3D displacement fields / Frank Liebold in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkPotential of texture from SAR tomographic images for forest aboveground biomass estimation / Zhanmang Liao in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)PermalinkSubpixel SAR image registration through parabolic interpolation of the 2-D cross correlation / Luca Pallotta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkTraffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning / Yann Méneroux in International Journal of Data Science and Analytics JDSA, vol 10 n° 1 (June 2020)PermalinkUnsupervised change detection between SAR images based on hypergraphs / Jun Wang in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkA water identification method basing on grayscale Landsat 8 OLI images / Zhitian Deng in Geocarto international, vol 35 n° 7 ([15/05/2020])PermalinkAssessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery / Allison Lassiter in Plos one, vol 15 n° 5 (May 2020)PermalinkAutomatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)PermalinkA convolutional neural network with mapping layers for hyperspectral image classification / Rui Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkDeep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM Transactions on spatial algorithms and systems, TOSAS, vol 6 n° 3 (May 2020)PermalinkDiscrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF) / Su Shu in Marine geodesy, Vol 43 n° 3 (May 2020)PermalinkGeomorphic Change Detection Using Cost-Effective Structure-from-Motion Photogrammetry: Evaluation of Direct Georeferencing from Consumer-Grade UAS at Orewa Beach (New Zealand) / Stéphane Bertin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 5 (May 2020)PermalinkMapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics / E. Banzhaf in Geocarto international, vol 35 n° 6 ([01/05/2020])PermalinkModeling strawberry biomass and leaf area using object-based analysis of high-resolution images / Zhen Guan in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)PermalinkOutlier detection and robust plane fitting for building roof extraction from LiDAR data / Emon Kumar Dey in International Journal of Remote Sensing IJRS, vol 41 n° 16 (01-10 May 2020)PermalinkA point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkRegion level SAR image classification using deep features and spatial constraints / Anjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)PermalinkAdaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation / Deliang Xiang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkAutomated terrain feature identification from remote sensing imagery: a deep learning approach / Wenwen Li in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)PermalinkBuilding Extraction from High-Resolution Remote Sensing Images Based on GrabCut with Automatic Selection of Foreground and Background Samples / Ka Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 4 (April 2020)PermalinkConterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database / Collin Homer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkDeformation detection through the realization of reference frames / Nestoras Papadopoulos in Journal of applied geodesy, vol 14 n° 2 (April 2020)PermalinkGeocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkGIS-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)PermalinkMultichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization / Puhong Duan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkTechniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network / Adil Alim in Geoinformatica, vol 24 n° 2 (April 2020)PermalinkUse of automated change detection and VGI sources for identifying and validating urban land use change / Ana-Maria Olteanu-Raimond in Remote sensing, vol 12 n° 7 (April 2020)PermalinkExtracting impervious surfaces from full polarimetric SAR images in different urban areas / Sara Attarchi in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)PermalinkA novel nonlinear hyperspectral unmixing approach for images of oil spills at sea / Ying Li in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)PermalinkDimension reduction methods applied to coastline extraction on hyperspectral imagery / Ozan Arslan in Geocarto international, vol 35 n° 4 ([15/03/2020])PermalinkAn improved RANSAC algorithm for extracting roof planes from airborne lidar data / Sibel Canaz Sevgen in Photogrammetric record, vol 35 n° 169 (March 2020)PermalinkAssessing environmental impacts of urban growth using remote sensing / John C. Trinder in Geo-spatial Information Science, vol 23 n° 1 (March 2020)PermalinkAssessing the shape accuracy of coarse resolution burned area identifications / Michael L. Humber in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkAssessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 / Anthony Campbell in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)PermalinkAssessment of the Baspa basin glaciers mass budget using different remote sensing methods and modeling techniques / Vinay Kumar Gaddam in Geocarto international, vol 35 n° 3 ([01/03/2020])PermalinkClassification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network / Yueguan Yan in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkA discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkEdge-reinforced convolutional neural network for road detection in very-high-resolution remote sensing imagery / Xiaoyan Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)PermalinkEfficient match pair selection for oblique UAV images based on adaptive vocabulary tree / San Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkHeuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data / Xiuyuan Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkIntegrated edge detection and terrain analysis for agricultural terrace delineation from remote sensing images / Wen Dai in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)PermalinkIntegration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model / Nadeem Fareed in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkReducing shadow effects on the co-registration of aerial image pairs / Matthew Plummer in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)PermalinkSea-land segmentation using deep learning techniques for Landsat-8 OLI imagery / Ting Yang in Marine geodesy, Vol 43 n° 2 (March 2020)PermalinkSimultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkSpectral–spatial–temporal MAP-based sub-pixel mapping for land-cover change detection / Da He in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkThe application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study / Yanan Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)PermalinkUnsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)PermalinkAutomated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkComputer vision-based framework for extracting tectonic lineaments from optical remote sensing data / Ehsan Farahbakhsh in International Journal of Remote Sensing IJRS, vol 41 n°5 (01 - 08 février 2020)PermalinkMulti-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering / Liyuan Ma in European journal of remote sensing, vol 53 n° 1 (2020)PermalinkA novel fire index-based burned area change detection approach using Landsat-8 OLI data / Sicong Liu in European journal of remote sensing, vol 53 n° 1 (2020)PermalinkThree-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering / Shangpeng Sun in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkCombining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods / Liheng Peng in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)PermalinkExtracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model / Xiaoping Wang in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)PermalinkSpatial visualization of quantitative landscape changes in an industrial region between 1827 and 1883. Case study Katowice, southern Poland / Paweł Cybulski in Journal of maps, vol 16 n° 1 ([02/01/2020])Permalink10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 / Jean-Baptiste Thomas (2020)Permalink3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)PermalinkPermalinkAdvances in Intelligent Data Analysis XVIII : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29 2020 / Michael R. Berthold (2020)PermalinkAnalyse automatique du couvert végétal pour la gestion du risque végétation en milieu ferroviaire à partir d'imagerie aérienne / Hélène Rouillon (2020)PermalinkAnalyse, structuration et sémantisation des images aériennes [diaporama] / Valérie Gouet-Brunet (2020)PermalinkApplication of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)PermalinkApplication of machine learning techniques for evidential 3D perception, in the context of autonomous driving / Edouard Capellier (2020)PermalinkPermalinkAutocovariance-based perceptual textural features corresponding to human visual perception / N. Abbadeni (2020)Permalink