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Learning-based superresolution land cover mapping / Feng Ling in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
[article]
Titre : Learning-based superresolution land cover mapping Type de document : Article/Communication Auteurs : Feng Ling, Auteur ; Yihang Zhang, Auteur ; Giles M. Foody, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 3794 - 3810 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme d'apprentissage
[Termes IGN] base de données localisées
[Termes IGN] géovisualisation
[Termes IGN] image à très haute résolution
[Termes IGN] occupation du sol
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] représentation des donnéesRésumé : (Auteur) Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarse-spatial-resolution fraction images estimated by soft classification. The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose prior model is learned from other available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent fine-spatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment. Numéro de notice : A2016-872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2527841 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2527841 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83029
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 3794 - 3810[article]An assessment of algorithmic parameters affecting image classification accuracy by random forests / Dee Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)
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Titre : An assessment of algorithmic parameters affecting image classification accuracy by random forests Type de document : Article/Communication Auteurs : Dee Shi, Auteur ; Xiaojun Yang, Auteur Année de publication : 2016 Article en page(s) : pp 407 - 417 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] classification
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] impact sur les données
[Termes IGN] occupation du sol
[Termes IGN] précision de la classificationRésumé : (Auteur) Random forests as a promising ensemble learning algorithm have been increasingly used for remote sensor image classification, and are found to perform identical or better than some popular classifiers. With only two algorithmic parameters, they are relatively easier to implement. Existing literature suggests that the performance of random forests is insensitive to changing algorithmic parameters. However, this was largely based on the classifier's accuracy that does not necessarily represent the resulting thematic map accuracy. The current study extends beyond the classifier's accuracy assessment and investigate how the algorithmic parameters could affect the resulting thematic map accuracy by random forests. A set of random forest models with different parameter settings was carefully constructed and then used to classify a satellite image into multiple land cover categories. Both the classifier's accuracy and the map accuracy were assessed. The results reveal that these parameters can affect the map accuracy up to 9 ∼16 percent for some classes, although their impact on the classifier's accuracy was quite limited. A careful parameterization prioritizing thematic map accuracy can help improve the performance of random forests in image classification, especially for spectrally complex land cover classes. These findings can help establish practical guidance on the use of random forests in the remote sensing community. Numéro de notice : A2016-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.407 En ligne : http://dx.doi.org/10.14358/PERS.82.6.407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81345
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 407 - 417[article]Learning grammars for architecture-specific facade parsing / Raghudeep Gadde in International journal of computer vision, vol 117 n° 3 (May 2016)
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Titre : Learning grammars for architecture-specific facade parsing Type de document : Article/Communication Auteurs : Raghudeep Gadde, Auteur ; Renaud Marlet, Auteur ; Nikos Paragios, Auteur Année de publication : 2016 Article en page(s) : pp 290 – 316 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse de données
[Termes IGN] apprentissage automatique
[Termes IGN] architecture
[Termes IGN] façade
[Termes IGN] réalité de terrainRésumé : (auteur) Parsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four different datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework. Numéro de notice : A2016--149 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-016-0887-4 En ligne : https://doi.org/10.1007/s11263-016-0887-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85917
in International journal of computer vision > vol 117 n° 3 (May 2016) . - pp 290 – 316[article]Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper) / Ran Wang in Geoinformatica, vol 20 n° 2 (April - June 2016)
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Titre : Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper) Type de document : Article/Communication Auteurs : Ran Wang, Auteur ; Chi-Yin Chow, Auteur ; Yan Lyu, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 327 - 349 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme d'apprentissage
[Termes IGN] apprentissage dirigé
[Termes IGN] comportement
[Termes IGN] données massives
[Termes IGN] exploration de données
[Termes IGN] histogramme
[Termes IGN] point d'intérêt
[Termes IGN] positionnement automatique
[Termes IGN] téléphonie mobile
[Termes IGN] utilisateurRésumé : (auteur) Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method. Numéro de notice : A2016-375 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article DOI : 10.1007/s10707-015-0237-7 En ligne : http://dx.doi.org/10.1007/s10707-015-0237-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81140
in Geoinformatica > vol 20 n° 2 (April - June 2016) . - pp 327 - 349[article]
Titre : Statistical learning from a regression perspective Type de document : Guide/Manuel Auteurs : Richard A. Berk, Auteur Editeur : Springer International Publishing Année de publication : 2016 ISBN/ISSN/EAN : 978-3-319-44048-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de données
[Termes IGN] arbre aléatoire
[Termes IGN] classification et arbre de régression
[Termes IGN] ensachage
[Termes IGN] régression
[Termes IGN] régression multivariée par spline adaptative
[Termes IGN] régression par quantile
[Termes IGN] séparateur à vaste margeRésumé : (éditeur) This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. A principal instance is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Also provided is helpful craft lore such as not automatically ceding data analysis decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important message is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R with code routinely provided. Note de contenu : 1- Statistical Learning as a Regression Problem
2- Splines, Smoothers, and Kernels
3- Classification and Regression Trees (CART)
4- Bagging
5- Random Forests
6- Boosting
7- Support Vector Machines
8- Some Other Procedures Briefly
9- Broader Implications and a Bit of Craft LoreNuméro de notice : 25800 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel de cours DOI : 10.1007/978-3-319-44048-4 En ligne : https://doi.org/10.1007/978-3-319-44048-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95043 Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing / Jerome O’Connell in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)PermalinkAn intelligent spatial proximity system using neurofuzzy classifiers and contextual information / F. Barouni in Geomatica, vol 69 n° 3 (september 2015)PermalinkSpectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)PermalinkUn environnement collaboratif pour l’acquisition de compétences en conception-développement d’applications centrées utilisateur. Application aux systèmes d'assitance à la santé et au bien-être / Maha Khemaja in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)PermalinkSpatial-aware dictionary learning for hyperspectral image classification / Ali Soltani-Farani in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkHyperspectral remote sensing image subpixel target detection based on supervised metric learning / Lefei Zhang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)PermalinkActive learning in the spatial domain for remote sensing image classification / André Stumpf in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)PermalinkMise à jour d’une base de données d’occupation du sol à grande échelle en milieux naturels à partir d’une image satellite THR / Adrien Gressin (2014)PermalinkPanorama de l'intelligence artificielle, ses bases méthodologiques, ses développements, 2. Algorithmes pour l'intelligence artificielle / Pierre Marquis (2014)PermalinkAn experimental comparison of semi-supervised learning algorithms for multispectral image classification / Enmei Tu in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 4 (April 2013)Permalink