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Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping / Wojciech Drzewiecki in Geodesy and cartography, vol 66 n° 2 (December 2017)
[article]
Titre : Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping Type de document : Article/Communication Auteurs : Wojciech Drzewiecki, Auteur Année de publication : 2017 Article en page(s) : pp 171 - 210 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] bassin hydrographique
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] image Landsat
[Termes IGN] modèle de régression
[Termes IGN] Pologne
[Termes IGN] surface imperméableRésumé : (auteur) We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory. Numéro de notice : A2017-787 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/geocart-2017-0012 En ligne : https://doi.org/10.1515/geocart-2017-0012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89099
in Geodesy and cartography > vol 66 n° 2 (December 2017) . - pp 171 - 210[article]Unsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification / Yiting Tao in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)
[article]
Titre : Unsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification Type de document : Article/Communication Auteurs : Yiting Tao, Auteur ; Miaozhong Xu, Auteur ; Fan Zhang, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 6805 - 6823 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] classification pixellaire
[Termes IGN] déconvolution
[Termes IGN] image Geoeye
[Termes IGN] image Quickbird
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) As the acquisition of very high resolution (VHR) satellite images becomes easier owing to technological advancements, ever more stringent requirements are being imposed on automatic image interpretation. Moreover, per-pixel classification has become the focus of research interests in this regard. However, the efficient and effective processing and the interpretation of VHR satellite images remain a critical task. Convolutional neural networks (CNNs) have recently been applied to VHR satellite images with considerable success. However, the prevalent CNN models accept input data of fixed sizes and train the classifier using features extracted directly from the convolutional stages or the fully connected layers, which cannot yield pixel-to-pixel classifications. Moreover, training a CNN model requires large amounts of labeled reference data. These are challenging to obtain because per-pixel labeled VHR satellite images are not open access. In this paper, we propose a framework called the unsupervised-restricted deconvolutional neural network (URDNN). It can solve these problems by learning an end-to-end and pixel-to-pixel classification and handling a VHR classification using a fully convolutional network and a small number of labeled pixels. In URDNN, supervised learning is always under the restriction of unsupervised learning, which serves to constrain and aid supervised training in learning more generalized and abstract feature. To some degree, it will try to reduce the problems of overfitting and undertraining, which arise from the scarcity of labeled training data, and to gain better classification results using fewer training samples. It improves the generality of the classification model. We tested the proposed URDNN on images from the Geoeye and Quickbird sensors and obtained satisfactory results with the highest overall accuracy (OA) achieved as 0.977 and 0.989, respectively. Experiments showed that the combined effects of additional kernels and stages may have produced better results, and two-stage URDNN consistently produced a more stable result. We compared URDNN with four methods and found that with a small ratio of selected labeled data items, it yielded the highest and most stable results, whereas the accuracy values of the other methods quickly decreased. For some categories with fewer training pixels, accuracy for categories from other methods was considerably worse than that in URDNN, with the largest difference reaching almost 10%. Hence, the proposed URDNN can successfully handle the VHR image classification using a small number of labeled pixels. Furthermore, it is more effective than state-of-the-art methods. Numéro de notice : A2017-766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2734697 En ligne : https://doi.org/10.1109/TGRS.2017.2734697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88803
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 6805 - 6823[article]A batch-mode regularized multimetric active learning framework for classification of hyperspectral images / Zhou Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
[article]
Titre : A batch-mode regularized multimetric active learning framework for classification of hyperspectral images Type de document : Article/Communication Auteurs : Zhou Zhang, Auteur ; Melba M. Crawford, Auteur Année de publication : 2017 Article en page(s) : pp 6594 - 6609 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classificationRésumé : (Auteur) Techniques that combine multiple types of features, such as spectral and spatial features, for hyperspectral image classification can often significantly improve the classification accuracy and produce a more reliable thematic map. However, the high dimensionality of the input data and the typically limited quantity of labeled samples are two key challenges that affect classification performance of supervised methods. In order to simultaneously deal with these issues, a regularized multimetric active learning (AL) framework is proposed which consists of three main parts. First, a regularized multimetric learning approach is proposed to jointly learn distinct metrics for different types of features. The regularizer incorporates the unlabeled data based on the neighborhood relationship, which helps avoid overfitting at early stages of AL, when the quantity of training data is particularly small. Then, as AL proceeds, the regularizer is also updated through similarity propagation, thus taking advantage of informative labeled samples. Finally, multiple features are projected into a common feature space, in which a new batch-mode AL strategy combining uncertainty and diversity is utilized in conjunction with k-nearest neighbor classification to enrich the set of labeled samples. In order to evaluate the effectiveness of the proposed framework, the experiments were conducted on two benchmark hyperspectral data sets, and the results were compared to those achieved by several other state-of-the-art AL methods. Numéro de notice : A2017-760 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2730583 En ligne : https://doi.org/10.1109/TGRS.2017.2730583 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88788
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6594 - 6609[article]Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning / Yimin Chen in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)
[article]
Titre : Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning Type de document : Article/Communication Auteurs : Yimin Chen, Auteur ; Xiaoping Liu, Auteur ; Xia Li, Auteur Année de publication : 2017 Article en page(s) : pp 2480 - 2504 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] automate cellulaire
[Termes IGN] changement d'occupation du sol
[Termes IGN] croissance urbaine
[Termes IGN] données vectorielles
[Termes IGN] étalonnage de modèle
[Termes IGN] Kouangtoung (Chine)
[Termes IGN] maillage
[Termes IGN] parcelle cadastrale
[Termes IGN] simulationRésumé : (Auteur) The reliability of raster cellular automaton (CA) models for fine-scale land change simulations has been increasingly questioned, because regular pixels/grids cannot precisely represent irregular geographical entities and their interactions. Vector CA models can address these deficiencies due to the ability of the vector data structure to represent realistic urban entities. This study presents a new land parcel cellular automaton (LP-CA) model for simulating urban land changes. The innovation of this model is the use of ensemble learning method for automatic calibration. The proposed model is applied in Shenzhen, China. The experimental results indicate that bagging-Naïve Bayes yields the highest calibration accuracy among a set of selected classifiers. The assessment of neighborhood sensitivity suggests that the LP-CA model achieves the highest simulation accuracy with neighbor radius r = 2. The calibrated LP-CA is used to project future urban land use changes in Shenzhen, and the results are found to be consistent with those specified in the official city plan. Numéro de notice : A2017-702 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1367004 En ligne : https://doi.org/10.1080/13658816.2017.1367004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88083
in International journal of geographical information science IJGIS > vol 31 n° 11-12 (November - December 2017) . - pp 2480 - 2504[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017061 RAB Revue Centre de documentation En réserve L003 Disponible 079-2017062 RAB Revue Centre de documentation En réserve L003 Disponible A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data / Qunying Huang in Computers, Environment and Urban Systems, vol 66 (November 2017)
[article]
Titre : A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data Type de document : Article/Communication Auteurs : Qunying Huang, Auteur ; Guido Cervone, Auteur ; Guiming Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 23 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] caractérisation
[Termes IGN] catastrophe naturelle
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] exploration de données géographiques
[Termes IGN] exploration de texte
[Termes IGN] image numérique
[Termes IGN] informatique en nuage
[Termes IGN] inondation
[Termes IGN] intégration de données
[Termes IGN] interface web
[Termes IGN] prototype
[Termes IGN] tempête
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Social media streams and remote sensing data have emerged as new sources for tracking disaster events, and assessing their damages. Previous studies focus on a case-by-case approach, where a specific event was first chosen and filtering criteria (e.g., keywords, spatiotemporal information) are manually designed and used to retrieve relevant data for disaster analysis. This paper presents a framework that synthesizes multi-sourced data (e.g., social media, remote sensing, Wikipedia, and Web), spatial data mining and text mining technologies to build an architecturally resilient and elastic solution to support disaster analysis of historical and future events. Within the proposed framework, Wikipedia is used as a primary source of different historical disaster events, which are extracted to build an event database. Such a database characterizes the salient spatiotemporal patterns and characteristics of each type of disaster. Additionally, it can provide basic semantics, such as event name (e.g., Hurricane Sandy) and type (e.g., flooding) and spatiotemporal scopes, which are then tuned by the proposed procedures to extract additional information (e.g., hashtags for searching tweets), to query and retrieve relevant social media and remote sensing data for a specific disaster. Besides historical event analysis and pattern mining, the cloud-based framework can also support real-time event tracking and monitoring by providing on-demand and elastic computing power and storage capabilities. A prototype is implemented and tested with data relative to the 2011 Hurricane Sandy and the 2013 Colorado flooding. Numéro de notice : a2017-430 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2017.06.004 En ligne : https://doi.org/10.1016/j.compenvurbsys.2017.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86330
in Computers, Environment and Urban Systems > vol 66 (November 2017) . - pp 23 - 37[article]Hybrid image noise reduction algorithm based on genetic ant colony and PCNN / Chong Shen in The Visual Computer, vol 33 n° 11 (November 2017)PermalinkKnowledge-guided consistent correlation analysis of multimode landslide monitoring data / Shuangxi Miao in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)PermalinkSocial Distance metric: from coordinates to neighborhoods / Vagan Terziyan in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)PermalinkSparse bayesian learning-based time-variant deconvolution / Sanyi Yuan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkAn iterative method for obtaining a mean 3D axis from a set of GNSS traces for use in positional controls / A. Mozas-Calvache in Survey review, vol 49 n° 355 (October 2017)PermalinkDiscovering non-compliant window co-occurrence patterns / Reem Y. Ali in Geoinformatica, vol 21 n° 4 (October - December 2017)PermalinkA geometric correspondence feature based-mismatch removal in vision based-mapping and navigation / Zeyu Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)PermalinkAtmospheric correction over coastal waters using multilayer neural networks / Yongzhen Fan in Remote sensing of environment, vol 199 (15 September 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)Permalink