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Semi-automatic extraction of rural roads under the constraint of combined geometric and texture features / Hai Tan in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)
[article]
Titre : Semi-automatic extraction of rural roads under the constraint of combined geometric and texture features Type de document : Article/Communication Auteurs : Hai Tan, Auteur ; Zimo Shen, Auteur ; Jiguang Dai, Auteur Année de publication : 2021 Article en page(s) : pp 754 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] chemin rural
[Termes IGN] Chine
[Termes IGN] coefficient de corrélation
[Termes IGN] contrainte géométrique
[Termes IGN] corrélation croisée normalisée
[Termes IGN] courbure
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction semi-automatique
[Termes IGN] image à haute résolution
[Termes IGN] modèle de simulation
[Termes IGN] niveau de gris (image)
[Termes IGN] route
[Termes IGN] texture d'imageRésumé : (auteur) The extraction of road information from high-resolution remotely-sensed images has important application value in many fields. Rural roads have the characteristics of relatively narrow widths and diversified pavement materials; these characteristics can easily lead to problems involving the similarity of the road texture with the texture of surrounding objects and make it difficult to improve the automation of traditional high-precision road extraction methods. Based on this background, a semi-automatic rural road extraction method constrained by a combination of geometric and texture features is proposed in this paper. First, an adaptive road width extraction model is proposed to improve the accuracy of the initial road centre point. Then, aiming at the continuous change of curvature of rural roads, a tracking direction prediction model is proposed. Finally, a matching model under geometric texture constraints is proposed, which solves the problem of similarity between road and neighbourhood texture to a certain extent. The experimental results show that by selecting different types of experimental scenes or remotely sensed image data, compared with other methods, the proposed method can not only guarantee the road extraction accuracy but also improve the degree of automation to a certain extent. Numéro de notice : A2021-850 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10110754 Date de publication en ligne : 09/11/2021 En ligne : https://doi.org/10.3390/ijgi10110754 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99009
in ISPRS International journal of geo-information > vol 10 n° 11 (November 2021) . - pp 754[article]Adaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)
[article]
Titre : Adaptive edge preserving maps in Markov random fields for hyperspectral image classification Type de document : Article/Communication Auteurs : Chao Pan, Auteur ; Xiuping Jia, Auteur ; Jie Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8568 - 8583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] accentuation de contours
[Termes IGN] algorithme Graph-Cut
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classe d'objets
[Termes IGN] détection de contours
[Termes IGN] étiquette de classe
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] segmentation d'imageRésumé : (auteur) This article presents a novel adaptive edge preserving (aEP) scheme in Markov random fields (MRFs) for hyperspectral image (HSI) classification. MRF regularization usually suffered from over-smoothing at boundaries and insufficient refinement within class objects. This work divides and conquers this problem class-by-class, and integrates K ( K−1 )/2 ( K is the class number) aEP maps (aEPMs) in MRF model. Spatial label dependence measure (SLDM) is designed to estimate the interpixel label dependence for given spectral similarity measure. For each class pair, aEPM is optimized by maximizing the difference between intraclass and interclass SLDM. Then, aEPMs are integrated with multilevel logistic (MLL) model to regularize the raw pixelwise labeling obtained by spectral and spectral–spatial methods, respectively. The graph-cuts-based α β -swap algorithm is modified to optimize the designed energy function. Moreover, to evaluate the final refined results at edges and small details thoroughly, segmentation evaluation metrics are introduced. Experiments conducted on real HSI data denote the superiority of aEPMs in evaluation metrics and region consistency, especially in detail preservation. Numéro de notice : A2021-713 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3035642 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3035642 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98618
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8568 - 8583[article]A deep multi-modal learning method and a new RGB-depth data set for building roof extraction / Mehdi Khoshboresh Masouleh in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)
[article]
Titre : A deep multi-modal learning method and a new RGB-depth data set for building roof extraction Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2021 Article en page(s) : pp 759 - 766 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] détection du bâti
[Termes IGN] données multisources
[Termes IGN] effet de profondeur cinétique
[Termes IGN] empreinte
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image RVB
[Termes IGN] Indiana (Etats-Unis)
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal profond
[Termes IGN] segmentation d'image
[Termes IGN] superpixel
[Termes IGN] toitRésumé : (Auteur) This study focuses on tackling the challenge of building mapping in multi-modal remote sensing data by proposing a novel, deep superpixel-wise convolutional neural network called DeepQuantized-Net, plus a new red, green, blue (RGB)-depth data set named IND. DeepQuantized-Net incorporated two practical ideas in segmentation: first, improving the object pattern with the exploitation of superpixels instead of pixels, as the imaging unit in DeepQuantized-Net. Second, the reduction of computational cost. The generated data set includes 294 RGB-depth images (256 training images and 38 test images) from different locations in the state of Indiana in the U.S., with 1024 × 1024 pixels and a spatial resolution of 0.5 ftthat covers different cities. The experimental results using the IND data set demonstrates the mean F1 scores and the average Intersection over Union scores could increase by approximately 7.0% and 7.2% compared to other methods, respectively. Numéro de notice : A2021-677 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00007R2 Date de publication en ligne : 01/10/2021 En ligne : https://doi.org/10.14358/PERS.21-00007R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98878
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 759 - 766[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021101 SL Revue Centre de documentation Revues en salle Disponible Disaster Image Classification by Fusing Multimodal Social Media Data / Zhiqiang Zou in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)
[article]
Titre : Disaster Image Classification by Fusing Multimodal Social Media Data Type de document : Article/Communication Auteurs : Zhiqiang Zou, Auteur ; Hongyu Gan, Auteur ; Qunying Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 636 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse visuelle
[Termes IGN] apprentissage profond
[Termes IGN] catastrophe naturelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] corpus
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] données massives
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données multisource
[Termes IGN] qualité des données
[Termes IGN] traitement de donnéesNuméro de notice : A2021-803 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10100636 Date de publication en ligne : 24/09/2021 En ligne : https://doi.org/10.3390/ijgi10100636 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98856
in ISPRS International journal of geo-information > vol 10 n° 10 (October 2021) . - n° 636[article]Disaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)
[article]
Titre : Disaster intensity-based selection of training samples for remote sensing building damage classification Type de document : Article/Communication Auteurs : Luis Moya, Auteur ; Christian Geiss, Auteur ; Masakazu Hashimoto, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8288 - 8304 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] déformation d'édifice
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] dommage matériel
[Termes IGN] données de terrain
[Termes IGN] échantillonnage de données
[Termes IGN] image optique
[Termes IGN] inondation
[Termes IGN] séismeRésumé : (auteur) Previous applications of machine learning in remote sensing for the identification of damaged buildings in the aftermath of a large-scale disaster have been successful. However, standard methods do not consider the complexity and costs of compiling a training data set after a large-scale disaster. In this article, we study disaster events in which the intensity can be modeled via numerical simulation and/or instrumentation. For such cases, two fully automatic procedures for the detection of severely damaged buildings are introduced. The fundamental assumption is that samples that are located in areas with low disaster intensity mainly represent nondamaged buildings. Furthermore, areas with moderate to strong disaster intensities likely contain damaged and nondamaged buildings. Under this assumption, a procedure that is based on the automatic selection of training samples for learning and calibrating the standard support vector machine classifier is utilized. The second procedure is based on the use of two regularization parameters to define the support vectors. These frameworks avoid the collection of labeled building samples via field surveys and/or visual inspection of optical images, which requires a significant amount of time. The performance of the proposed method is evaluated via application to three real cases: the 2011 Tohoku-Oki earthquake–tsunami, the 2016 Kumamoto earthquake, and the 2018 Okayama floods. The resulted accuracy ranges between 0.85 and 0.89, and thus, it shows that the result can be used for the rapid allocation of affected buildings. Numéro de notice : A2021-711 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3046004 Date de publication en ligne : 13/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3046004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98615
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8288 - 8304[article]Extracting 3D indoor maps with any shape accurately using building information modeling data / Qi Qiu in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkGPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings / Bin Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkA novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery / Massimiliano Pepe in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)PermalinkAutomatic building detection with polygonizing and attribute extraction from high-resolution images / Samitha Daranagama in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)PermalinkDouble adaptive intensity-threshold method for uneven Lidar data to extract road markings / Chengming Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)PermalinkThree-dimensional building change detection using object-based image analysis (case study: Tehran) / Fatemeh Tabib Mahmoudi in Applied geomatics, vol 13 n° 3 (September 2021)PermalinkUtilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)PermalinkMapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkCNN-based RGB-D salient object detection: Learn, select, and fuse / Hao Chen in International journal of computer vision, vol 129 n° 7 (July 2021)PermalinkFlood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)PermalinkMulti-scale coal fire detection based on an improved active contour model from Landsat-8 satellite and UAV images / Yanyan Gao in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)PermalinkResearch on 3D model reconstruction based on a sequence of cross-sectional images / Zhiguo Dong in Machine Vision and Applications, vol 32 n°4 (July 2021)PermalinkReview of spectral indices for urban remote sensing / Akib Javed in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)PermalinkSemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images / Daifeng Peng in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkUsing information entropy and a multi-layer neural network with trajectory data to identify transportation modes / Qingying Yu in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)PermalinkTowards efficient indoor/outdoor registration using planar polygons / Rahima Djahel in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkAn automatic workflow for orientation of historical images with large radiometric and geometric differences / Ferdinand Maiwald in Photogrammetric record, vol 36 n° 174 (June 2021)PermalinkDomain adaptive transfer attack-based segmentation networks for building extraction from aerial images / Younghwan Na in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkEfficient image dataset classification difficulty estimation for predicting deep-learning accuracy / Florian Scheidegger in The Visual Computer, vol 37 n° 6 (June 2021)PermalinkA high-resolution satellite DEM filtering method assisted with building segmentation / Yihui Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkMask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan / Dirk Tiede in Transactions in GIS, Vol 25 n° 3 (June 2021)PermalinkResearch on feature extraction method of indoor visual positioning image based on area division of foreground and background / Ping Zheng in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)PermalinkRobust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds / Reza Maalek in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkSemantic signatures for large-scale visual localization / Li Weng in Multimedia tools and applications, vol 80 n° 15 (June 2021)PermalinkAn area merging method in map generalization considering typical characteristics of structured geographic objects / Chengming Li in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)PermalinkLifting scheme-based sparse density feature extraction for remote sensing target detection / Ling Tian in Remote sensing, vol 13 n° 9 (May-1 2021)PermalinkMultiple convolutional features in Siamese networks for object tracking / Zhenxi Li in Machine Vision and Applications, vol 32 n° 3 (May 2021)PermalinkThe delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods / Akhtar Jamil in Geocarto international, vol 36 n° 7 ([15/04/2021])PermalinkUnsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkAutomated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours / Amir Hossein Safaie in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkA CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkExtraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkA geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkA novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)PermalinkL'oeil de l'espace / Anonyme in Géomètre, n° 2190 (avril 2021)PermalinkRotation-invariant feature learning in VHR optical remote sensing images via nested siamese structure with double center loss / Ruoqiao Jiang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkA skyline-based approach for mobile augmented reality / Mehdi Ayadi in The Visual Computer, vol 37 n° 4 (April 2021)PermalinkTree extraction and estimation of walnut structure parameters using airborne LiDAR data / Javier Estornell in International journal of applied Earth observation and geoinformation, vol 96 (April 2021)PermalinkVisual positioning in indoor environments using RGB-D images and improved vector of local aggregated descriptors / Longyu Zhang in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)PermalinkBasin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery / Zifeng Wang in Remote sensing of environment, Vol 255 (March 2021)PermalinkCharacterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkFeature detection and description for image matching: from hand-crafted design to deep learning / Lin Chen in Geo-spatial Information Science, vol 24 n° 1 (March 2021)PermalinkLearning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkMulti-level progressive parallel attention guided salient object detection for RGB-D images / Zhengyi Liu in The Visual Computer, vol 37 n° 3 (March 2021)PermalinkPassive radar imaging of ship targets with GNSS signals of opportunity / Debora Pastina in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkSaline-soil deformation extraction based on an improved time-series InSAR approach / Wei Xiang in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkAutomatic filtering and 2D modeling of airborne laser scanning building point cloud / Fayez Tarsha-Kurdi in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkCurved buildings reconstruction from airborne LiDAR data by matching and deforming geometric primitives / Jingwei Song in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkSAR image speckle reduction based on nonconvex hybrid total variation model / Yuli Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkAutomated detection of individual Juniper tree location and forest cover changes using Google Earth Engine / Sudeera Wickramarathna in Annals of forest research, vol 64 n° 1 (2021)PermalinkAutomatic object extraction from airborne laser scanning point clouds for digital base map production / Elyta Widyaningrum (2021)PermalinkBuilding extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)PermalinkConnecting images through time and sources: Introducing low-data, heterogeneous instance retrieval / Dimitri Gominski (2021)PermalinkContributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)PermalinkDétection/reconnaissance d'objets urbains à partir de données 3D multicapteurs prises au niveau du sol, en continu / Younes Zegaoui (2021)PermalinkExtraction of street pole-like objects based on plane filtering from mobile LiDAR data / Jingming Tu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkFlood mapping from radar remote sensing using automated image classification techniques / Lisa Landuyt (2021)PermalinkFuNet: A novel road extraction network with fusion of location data and remote sensing imagery / Kai Zhou in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)PermalinkGeometric and semantic joint approach for the reconstruction of digital models of buildings / Pierre-Alain Langlois (2021)PermalinkGeometric computer vision: omnidirectional visual and remotely sensed data analysis / Pouria Babahajiani (2021)PermalinkImage matching from handcrafted to deep features: A survey / Jiayi Ma in International journal of computer vision, vol 29 n° 1 (January 2021)PermalinkLANet: Local attention embedding to improve the semantic segmentation of remote sensing images / Lei Ding in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkPermalinkProposition d’un référentiel de description et de détection de la végétation dans une agglomération / Mathilde Segaud (2021)PermalinkRelation-constrained 3D reconstruction of buildings in metropolitan areas from photogrammetric point clouds / Yuan Li in Remote sensing, vol 13 n° 1 (January-1 2021)PermalinkSteps-based tree crown delineation by analyzing local minima for counting the trees in very high resolution satellite imagery / Debasish Chakraborty in Geocarto international, vol 36 n° 1 ([01/01/2021])PermalinkStudy of an integrated pre-processing architecture for smart-imaging-systems, in the context of lowpower computer vision and embedded object detection / Luis Cubero Montealegre (2021)PermalinkUrban construction waste with VHR remote sensing using multi-feature analysis and a hierarchical segmentation method / Qiang Chen in Remote sensing, vol 13 n° 1 (January-1 2021)PermalinkVectorization of historical maps using deep edge filtering and closed shape extraction / Yizi Chen (2021)PermalinkAutomatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)PermalinkMS-RRFSegNetMultiscale regional relation feature segmentation network for semantic segmentation of urban scene point clouds / Haifeng Luo in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkBuilding change detection using a shape context similarity model for LiDAR data / Xuzhe Lyu in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkA deep learning framework for matching of SAR and optical imagery / Lloyd Haydn Hughes in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkHigh-resolution remote sensing image scene classification via key filter bank based on convolutional neural network / Fengpeng Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkTopographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])PermalinkTextural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])PermalinkChoosing an appropriate training set size when using existing data to train neural networks for land cover segmentation / Huan Ning in Annals of GIS, vol 26 n° 4 (October 2020)PermalinkA graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkMultiview automatic target recognition for infrared imagery using collaborative sparse priors / Xuelu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkTree species classification using structural features derived from terrestrial laser scanning / Louise Terryn in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkCrater 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)PermalinkLocal color and morphological image feature based vegetation identification and its application to human environment street view vegetation mapping, or how green is our county? / Istvan G. Lauko in Geo-spatial Information Science, vol 23 n° 3 (September 2020)PermalinkA 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)PermalinkA novel deep network and aggregation model for saliency detection / Ye Liang in The Visual Computer, vol 36 n° 9 (September 2020)PermalinkPrecise 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)PermalinkSemi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (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)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])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)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)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)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])Permalink