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A novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification / Jing Lv in Geoinformatica, vol 24 n° 4 (October 2020)
[article]
Titre : A novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification Type de document : Article/Communication Auteurs : Jing Lv, Auteur ; Huimin Zhang, Auteur ; Ming Yang, Auteur ; Wanqi Yang, Auteur Année de publication : 2020 Article en page(s) : pp 827 - 848 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre aléatoire minimum
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification pixellaire
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'imageRésumé : (Auteur) The classification methods based on minimum spanning forest (MSF) have yielded impressive results for hyperspectral image. However, previous methods exist several drawbacks, i.e., marker selection methods are easily affected by boundary noise pixels, dissimilarity measure methods between pixels are inaccurate, and also image segmentation process is not robust, since they have not effectively utilized spatial information. To this end, in this paper, novel gradient-based marker selection technique, dissimilarity measures, and adaptive connection weighting method are proposed by making full use of spatial information in hyperspectral image. Concretely, for a given hyperspectral image, a pixel-wise classification is firstly performed, and meanwhile the gradient map is generated by a morphology-based algorithm. Secondly, the most reliable pixels are selected as the markers from the classification map, and then the boundary noise pixels are excluded from the marker map by using the gradient map. Thirdly, several new dissimilarity measures are proposed by incorporating gradient information or probability information of pixels. Furthermore, in the growth procedure of MSF, the connection weighting between pixels is adjusted adaptively to improve the robustness of the MSF algorithm. Finally, when building the final classification map by using the majority voting rule, the labels of the training samples are used to dominate the label prediction. Experimental results are performed on two hyperspectral image sets Indian Pines and University of Pavia with different resolutions and contexts. The proposed approach yields higher classification accuracies compared to previously proposed classification methods, and provides accurate segmentation maps. Numéro de notice : A2020-496 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00403-0 Date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.1007/s10707-020-00403-0 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96117
in Geoinformatica > vol 24 n° 4 (October 2020) . - pp 827 - 848[article]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)
[article]
Titre : Ship detection in SAR images via local contrast of Fisher vectors Type de document : Article/Communication Auteurs : Xueqian Wang, Auteur ; Gang Li, Auteur ; Xiao-Ping Zhang, Auteur ; You He, Auteur Année de publication : 2020 Article en page(s) : pp 6467 - 6479 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] contraste local
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] distribution de Fisher
[Termes IGN] fouillis d'échos
[Termes IGN] image radar moirée
[Termes IGN] navire
[Termes IGN] processus gaussien
[Termes IGN] rapport signal sur bruit
[Termes IGN] superpixelRésumé : (auteur) Existing superpixel-based detection algorithms for ship targets in synthetic aperture radar (SAR) images are often derived from the local contrast of intensities (i.e., the local contrast of the first-order information of superpixels) leading to deteriorating performance in low signal-to-clutter ratio (SCR) cases due to the low contrast between the intensities of targets and the clutter. In this article, we propose a new superpixel-based detector to improve the performance of ship target detection in SAR images via the local contrast of fisher vectors (LCFVs). The new LCFV-based detector exploits multiorder features of the superpixels based on the Gaussian mixture model (GMM) and accordingly improves the discrimination capability between the ship targets and the sea clutter, especially in low SCR cases. Experimental results demonstrate that the proposed LCFV-based detection algorithm provides better detection performance than the commonly used detection algorithms. Numéro de notice : A2020-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976880 Date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976880 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95713
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6467 - 6479[article]A 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)
[article]
Titre : A spaceborne SAR-based procedure to support the detection of landslides Type de document : Article/Communication Auteurs : Giuseppe Esposito, Auteur ; Ivan Marchesini, Auteur ; Alessandro Cesare Mondini, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2379 - 2395 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] algorithme de décalage moyen
[Termes IGN] cartographie des risques
[Termes IGN] correction d'image
[Termes IGN] détection de changement
[Termes IGN] effondrement de terrain
[Termes IGN] gestion des risques
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] ligne de rupture de pente
[Termes IGN] modèle de simulation
[Termes IGN] Papouasie-Nouvelle-Guinée
[Termes IGN] risque naturel
[Termes IGN] segmentation d'image
[Termes IGN] traitement automatique de donnéesRésumé : (auteur) The increasing availability of free-access satellite data represents a relevant opportunity for the analysis and assessment of natural hazards. The systematic acquisition of spaceborne imagery allows for monitoring areas prone to geohydrological disasters, providing relevant information for risk evaluation and management. In cases of major landslide events, for example, spaceborne radar data can provide an effective solution for the detection of slope failures, even in cases with persistent cloud cover. The information about the extension and location of the landslide-affected areas may support decision-making processes during emergency responses. In this paper, we present an automatic procedure based on Sentinel-1 Synthetic Aperture Radar (SAR) images, aimed at facilitating the detection of landslides over wide areas. Specifically, the procedure evaluates changes of radar backscattered signals associated with land cover modifications that may be also caused by mass movements. After a one-time calibration of some parameters, the processing chain is able to automatically execute the download and preprocessing of images, the detection of SAR amplitude changes, and the identification of areas potentially affected by landslides, which are then displayed in a georeferenced map. This map should help decision makers and emergency managers to organize field investigations. The process of automatization is implemented with specific scripts running on a GNU/Linux operating system and exploiting modules of open-source software. We tested the processing chain, in back analysis, on an area of about 3000 km2 in central Papua New Guinea that was struck by a severe seismic sequence in February–March 2018. In the area, we simulated a periodic survey of about 7 months, from 12 November 2017 to 6 June 2018, downloading 36 Sentinel-1 images and performing 17 change detection analyses automatically. The procedure resulted in statistical and graphical evidence of widespread land cover changes that occurred just after the most severe seismic events. Most of the detected changes can be interpreted as mass movements triggered by the seismic shaking. Numéro de notice : A2020-611 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/nhess-20-2379-2020 Date de publication en ligne : 10/09/2020 En ligne : https://doi.org/10.5194/nhess-20-2379-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95976
in Natural Hazards and Earth System Sciences > vol 20 n° 9 (September 2020) . - pp 2379 - 2395[article]Subpixel-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)
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Titre : Subpixel-pixel-superpixel-based multiview active learning for hyperspectral images classification Type de document : Article/Communication Auteurs : Yu Li, Auteur ; Ting Lu, Auteur ; Shutao Li, Auteur Année de publication : 2020 Article en page(s) : pp 4976 - 4988 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse infrapixellaire
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification pixellaire
[Termes IGN] échantillonnage
[Termes IGN] image hyperspectrale
[Termes IGN] image multiple
[Termes IGN] segmentation sémantique
[Termes IGN] superpixelRésumé : (auteur) Active learning (AL) attempts to actively select the most representative or useful training samples in an iterative manner. The aim is to simultaneously improve the classification performance and reduce the manual labeling effort. In this article, a novel subpixel-pixel-superpixel-based multiview AL (MAL) (SPS-MAL) method is proposed for hyperspectral image (HSI) classification. Here, the multiple views are generated via extracting the subpixel-level, pixel-level, and superpixel-level information. The multiple views can reflect various characteristics of HSI, i.e., spectral mixture, spectral discrimination, and spectral–spatial structure. Therefore, the joint use of diverse and complementary information in multiple views will contribute to a better identification ability of different classes. In addition, a coarse-to-fine MAL algorithm is introduced to effectively select the most representative samples with the most uncertainty. Specifically, a disagreement analysis on multiple views and joint posterior probability estimation is used to query unlabeled samples. Along with the expansion of training samples, view-specific confidence scores are estimated to adaptively integrate the classification results of multiple views, according to their discrimination performance. In this way, the classification accuracy will be further boosted while the number of necessary training samples can be significantly reduced. The experimental classification results on three well-known HSIs demonstrate the effectiveness of the proposed SPS-MAL method. Numéro de notice : A2020-392 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2971081 Date de publication en ligne : 14/02/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2971081 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95388
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4976 - 4988[article]Hyperspectral 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)
[article]
Titre : Hyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance Type de document : Article/Communication Auteurs : Bing Tu, Auteur ; Chengle Zhou, Auteur ; Danbing He, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4116 - 4131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] erreur d'échantillon
[Termes IGN] image hyperspectrale
[Termes IGN] pondération
[Termes IGN] précision de la classification
[Termes IGN] superpixelRésumé : (auteur) Classification is an important technique for remotely sensed hyperspectral image (HSI) exploitation. Often, the presence of wrong (noisy) labels presents a drawback for accurate supervised classification. In this article, we introduce a new framework for noisy label detection that combines a superpixel-to-pixel weighting distance (SPWD) and density peak clustering. The proposed method is able to accurately detect and remove noisy labels in the training set before HSI classification. It considers two weak assumptions when exploiting the spectral–spatial information contained in the HSI: 1) all the pixels in a superpixel belong to the same class and 2) close pixels in spectral space have the same label. The proposed method consists of the following steps. First, a superpixel segmentation step is used to obtain self-adaptive spatial information for each training sample. Then, a metric is utilized to measure the spectral distance information between each superpixel and pixel. Meanwhile, in order to overcome the first weak assumption, we use K nearest neighbors to obtain the closest neighborhoods of pixels around each superpixel, and a Gaussian weight is employed to mitigate the second weak assumption by adapting the original distance information. Next, the noisy labels in the original training set are removed by a density threshold-based decision function. Finally, the support vector machine (SVM) classifier is employed to evaluate the effectiveness of the proposed SPWD detection method in terms of classification accuracy. Experiments performed on several real HSI data sets demonstrate that the method can effectively improve the performance of classifiers trained with noisy training sets in terms of classification accuracy. Numéro de notice : A2020-283 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2961141 Date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2961141 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95105
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 4116 - 4131[article]Object-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])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)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)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)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)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)PermalinkSea-land segmentation using deep learning techniques for Landsat-8 OLI imagery / Ting Yang in Marine geodesy, Vol 43 n° 2 (March 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)PermalinkAnalyse automatique du couvert végétal pour la gestion du risque végétation en milieu ferroviaire à partir d'imagerie aérienne / Hélène Rouillon (2020)PermalinkApplication of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)PermalinkContribution à la segmentation et à la modélisation 3D du milieu urbain à partir de nuages de points / Tania Landes (2020)PermalinkDétection et vectorisation automatiqued’objets linéaires dans des nuages de points de voirie / Etienne Barçon (2020)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkLearning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)PermalinkSuperpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkUnsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters / Ting Mao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkResidences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkSegmenting mangrove ecosystems drone images using SLIC superpixels / Edward Zimudzi in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkOptimal segmentation of high spatial resolution images for the classification of buildings using random forests / James Bialas in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkMapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece / Maria Kampouri in Geocarto international, vol 34 n° 12 ([15/09/2019])PermalinkDevelopment and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkImplementing Moran eigenvector spatial filtering for massively large georeferenced datasets / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)PermalinkIntegration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain / Yolanda Torres in International journal of applied Earth observation and geoinformation, vol 81 (September 2019)PermalinkLearning and adapting robust features for satellite image segmentation on heterogeneous data sets / Sina Ghassemi in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkIndividual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])PermalinkUne nouvelle méthode de vectorisation du cadastre ancien / Antony Chalais in Géomatique expert, n° 129 (août - septembre 2019)PermalinkClassification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data / Prateek Verma in Geocarto international, vol 34 n° 10 ([15/07/2019])PermalinkA novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery / Tingting Wu in Advances in space research, vol 64 n°1 (1 July 2019)PermalinkSemantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)PermalinkAlbedo estimation for real-time 3D reconstruction using RGB-D and IR data / Patrick Stotko in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkJournées de la recherche 2019 / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)PermalinkSegmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective / Mohammad D. Hossain in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)Permalink3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction / Maximilian Brell in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkMethod for an automatic alignment of imagery and vector data applied to cadastral information in Poland / Juan J. Ruiz-Lendínez in Survey review, vol 51 n° 365 (March 2019)PermalinkPermalinkIndividual tree detection and crown delineation with 3D information from multi-view satellite Images / Changlin Xiao in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkPermalinkMéthodes d'apprentissage statistique pour la détection de la signalisation routière à partir de véhicules traceurs / Yann Méneroux (2019)PermalinkPotentialités de l’imagerie couleur embarquée pour la détection et la cartographie des maladies fongiques de la vigne / Florent Abdelghafour (2019)PermalinkSegmentation d'image par intégration itérative de connaissances / Mahaman Sani Chaibou Salaou (2019)PermalinkSimultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkAutomatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkChange detection based on stacked generalization system with segmentation constraint / Kun Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)PermalinkCoupling relationship among scale parameter, segmentation accuracy, and classification accuracy in GeOBIA / Ming Dongping in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)PermalinkIndividual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images / Fabien Hubert Wagner in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part B (November 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)PermalinkExtracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography / Lukas Roth in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkPredicting foreground object ambiguity and efficiently crowdsourcing the segmentation(s) / Danna Gurari in International journal of computer vision, vol 126 n° 7 (July 2018)PermalinkFusion tardive d’images SPOT 6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkIntegrated image matching and segmentation for 3D surface reconstruction in urban areas / Lei Ye in Photogrammetric Engineering & Remote Sensing, PERS, Vol 84 n° 3 (March 2018)PermalinkAdapting an existing semi-automatized image processing chain to enable Sentinel-2 data classification. / Hiyam Elbadri (2018)PermalinkCartographier l'occupation du sol à grande échelle : optimisation de la photo-interprétation par segmentation d'image / Maxime Vitter (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)PermalinkPermalinkLocalisation par l'image en milieu urbain : application à la réalité augmentée / Antoine Fond (2018)PermalinkSuperpixel partitioning of very high resolution satellite images for large-scale classification perspectives with deep convolutional neural networks / Tristan Postadjian (2018)PermalinkUtilisation de QGIS en télédétection, Ch. 2. Apports du MNT topo-bathymétrique pour l'évolution bio-géomorphologique des marais d'Ichkeul (Tunisie) / Zeineb Kassouk (2018)PermalinkLearning aggregated features and optimizing model for semantic labeling / Jianhua Wang in The Visual Computer, vol 33 n° 12 (December 2017)PermalinkSingle image dehazing via an improved atmospheric scattering model / Mingye Ju in The Visual Computer, vol 33 n° 12 (December 2017)PermalinkExtraction du bâti sur le territoire de la wilaya de Blida (Algérie) / Siham Bougdour in Géomatique expert, n° 119 (novembre - décembre 2017)PermalinkSalient object detection in complex scenes via D-S evidence theory based region classification / Chunlei Yang in The Visual Computer, vol 33 n° 11 (November 2017)PermalinkSpatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing / Xinyu Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkDenoising of natural images through robust wavelet thresholding and genetic programming / Asem Khmag in The Visual Computer, vol 33 n°9 (September 2017)PermalinkFacet segmentation-based line segment extraction for large-scale point clouds / Yangbin Lin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkSentinel-1A SAR and sentinel-2A MSI data fusion for urban ecosystem service mapping / Jan Haas in Remote Sensing Applications: Society and Environment, RSASE, vol 8 (November 2017)PermalinkLocal and global evaluation for remote sensing image segmentation / Tengfei Su in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkStructure from motion with line segments under relaxed endpoint constraints / Branislav Micusik in International journal of computer vision, vol 124 n° 1 (August 2017)PermalinkChange detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)PermalinkChange detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 2017)Permalink3D tree modeling from incomplete point clouds via optimization and L1-MST / Jie Mei in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkComplétion d'image exploitant des données multispectrales / Frédéric Bousefsaf in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)PermalinkEfficient edge-aware surface mesh reconstruction for urban scenes / András Bódis-Szomorú in Computer Vision and image understanding, vol 157 (April 2017)PermalinkIndustrialisation des processus d'extraction d'objets à partir de données photogrammétriques par drones / Jérémie Brossard in XYZ, n° 150 (mars - mai 2017)PermalinkObject-based water body extraction model using Sentinel-2 satellite imagery / Gordana Kaplan in European journal of remote sensing, vol 50 n° 1 (2017)PermalinkRaft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features / Wang Min in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)PermalinkMRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images / Ilias Grinias in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)PermalinkSkeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images / Zhihua Xua in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkReconstruction en 3D des bâtiments à partir des données Lidar / M. A. Missomi in Géomatique expert, n° 112 (septembre - octobre 2016)PermalinkA local structure and direction-aware optimization approach for three-dimensional tree modeling / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkMapping and characterization of hydrological dynamics in coastal marsh using high temporal resolution Sentinel-1 images / Cécile Cazals in Remote sensing, vol 8 n° 7 (July 2016)PermalinkDeep filter banks for texture recognition, description, and segmentation / Mircea Cimpoi in International journal of computer vision, vol 118 n° 1 (May 2016)PermalinkMapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data / Elisa Schäfer in Ecological indicators, vol 64 (May 2016)PermalinkMidrange geometric interactions for semantic segmentation / Julia Diebold in International journal of computer vision, vol 117 n° 3 (May 2016)PermalinkRobust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data / Abdul Nurunnabi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)PermalinkA feature selection approach for segmentation of very high-resolution satellite images / Ahmad Izadipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)PermalinkA land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology / Jin Xing in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)PermalinkOctree-based segmentation for terrestrial LiDAR point cloud data in industrial applications / Yun-Ting Su in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)PermalinkUniformity-based superpixel segmentation of hyperspectral images / Arun M. Saranathan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)Permalink