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A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification / Z. Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
[article]
Titre : A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification Type de document : Article/Communication Auteurs : Z. Wang, Auteur ; Liqiang Zhang, Auteur ; Tian Fang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2409 - 2425 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification orientée objet
[Termes IGN] détection de piéton
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] lasergrammétrie
[Termes IGN] objet mobile
[Termes IGN] semis de points
[Termes IGN] structure hiérarchique de données
[Termes IGN] télémétrie laser terrestre
[Termes IGN] zone urbaine denseRésumé : (Auteur) The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars. Numéro de notice : A2015-522 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2359951 En ligne : https://doi.org/10.1109/TGRS.2014.2359951 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77533
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2409 - 2425[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible Pattern-mining approach for conflating crowdsourcing road networks with POIs / Bisheng Yang in International journal of geographical information science IJGIS, vol 29 n° 5 (May 2015)
[article]
Titre : Pattern-mining approach for conflating crowdsourcing road networks with POIs Type de document : Article/Communication Auteurs : Bisheng Yang, Auteur ; Yunfei Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 786 - 805 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] acquisition de données
[Termes IGN] appariement de données localisées
[Termes IGN] conflation
[Termes IGN] données localisées
[Termes IGN] données localisées des bénévoles
[Termes IGN] données multisources
[Termes IGN] exploration de données
[Termes IGN] graphe
[Termes IGN] point d'intérêt
[Termes IGN] précision du positionnement
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] précision sémantique
[Termes IGN] qualité des données
[Termes IGN] réseau routier
[Termes IGN] squelettisationRésumé : (Auteur) Crowdsourcing geospatial data mainly collected by public citizens have brought about a profound transformation on data acquisition and utilization. However, the unpredictable positional accuracies, unstructured semantic descriptions, and invalid spatial relations occur to crowdsourcing geospatial data, causing difficulties for conflating heterogeneous data sets collected by different professional agencies or volunteers. We thus propose a novel pattern-mining approach to conflate crowdsourcing road networks with points of interest (POIs) geometrically and semantically. The proposed method mines the geometric patterns between road networks and POIs respectively and generates the pattern-related skeleton graphs for them. Then, corresponding points are determined between the two skeleton graphs to align POIs and road networks geometrically, and the road-related semantic data between the associated POIs and the road segments are compared to check the data quality of POIs and infer the road names of the road segments. Experimental results show the advantages of our proposed method, demonstrating a functional and promising solution for enriching POIs and road network geometrically and semantically. Numéro de notice : A2015-593 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.997238 En ligne : https://doi.org/10.1080/13658816.2014.997238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77883
in International journal of geographical information science IJGIS > vol 29 n° 5 (May 2015) . - pp 786 - 805[article]Refining high spatial resolution remote sensing image segmentation for man-made objects through acollinear and ipsilateral neighborhood model / Min Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)
[article]
Titre : Refining high spatial resolution remote sensing image segmentation for man-made objects through acollinear and ipsilateral neighborhood model Type de document : Article/Communication Auteurs : Min Wang, Auteur ; Yanxia Sun, Auteur ; Guanyi Chen, Auteur Année de publication : 2015 Article en page(s) : pp 397 - 406 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] détail topographique artificiel
[Termes IGN] détection d'objet
[Termes IGN] planimétrie
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] segmentation d'imageRésumé : (auteur) Man-made objects, such as buildings and roads, which are important targets for information extraction from high spatial resolution (HSR) remote sensing images, often feature straight boundaries. This study employs this knowledge on HSR image segmentation by embedding a straight-line constraint in regionbased image segmentation. A new concept called collinear and ipsilateral neighborhood is proposed and applied to hardboundary constraint-based image segmentation for accuracy improvement. In the experimental areas, the method accuracy measured by recall ratio r increases from 0.036 to 0.048 (on the average) after the refinement, with significantly smaller decreases in precision p that are all less than 0.006. In sum, the proposed technique effectively reduces over-segmentation errors and maintains the same level of under-segmentation error ratio, particularly in man-made areas. It facilitates subsequent objectbased image analyses, including feature extraction, object recognition, and classification. Numéro de notice : A2015-974 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : doi.org/10.14358/PERS.81.5.397 En ligne : https://doi.org/10.14358/PERS.81.5.397 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80044
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 5 (May 2015) . - pp 397 - 406[article]Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
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Titre : Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields Type de document : Article/Communication Auteurs : Junshi Xia, Auteur ; Jocelyn Chanussot, Auteur ; Peijun Du, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2532 - 2546 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse en composantes principales
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification et arbre de régression
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] performance
[Termes IGN] Rotation Forest classificationRésumé : (Auteur) In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification. Numéro de notice : A2015-519 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2361618 En ligne : https://doi.org/10.1109/TGRS.2014.2361618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77526
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2532 - 2546[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible Use of Landsat and Corona data for mapping forest cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil / Dan-Xia Song in ISPRS Journal of photogrammetry and remote sensing, vol 103 (May 2015)
[article]
Titre : Use of Landsat and Corona data for mapping forest cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil Type de document : Article/Communication Auteurs : Dan-Xia Song, Auteur ; Chengquan Huang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 81 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] Brésil
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte forestière
[Termes IGN] détection de changement
[Termes IGN] Etats-Unis
[Termes IGN] forêt tropicale
[Termes IGN] image Corona
[Termes IGN] image LandsatRésumé : (auteur) Land-cover change detection using satellite remote sensing is largely confined to the era of Landsat satellites, from 1972 to present. However, the Corona, Argon, and Lanyard intelligence satellites operated by the U.S. government between 1960 and 1972 have the potential to provide an important extension of the long-term record of Earth’s land surface. Recently declassified, the archive of images recorded by these satellites contains hundreds of thousands of photographs, many of which have very high ground resolution- 6–9 ft (1.8–2.7 m) even by today’s standards. This paper demonstrates methods for extending the span of forest-cover change analysis from the Landsat-5 and -7 era (1984 to present) to the previous era covered by the Corona archive in two study areas: one area covered predominantly by urban and sub-urban land uses in the eastern US and another area by tropical forest in central Brazil. We describe co-registration of Corona and Landsat images, extraction of texture features from Corona images, classification of Corona and Landsat images, and post-classification change detection based on the resulting thematic dataset. Second-order polynomial transformation of Corona images yielded geometric accuracy relative to Landsat-7 of 18.24 m for the urban area and 29.35 m for the tropical forest study area, generally deemed adequate for pixel-based change detection at Landsat resolution. Classification accuracies were approximately 95% and 96% for forest/non-forest discrimination for the temperate urban and tropical forest study areas, respectively. Texture within 7 × 7- to 9 × 9-pixel (∼13.0–16.5 m) neighborhoods and within 11 × 11-pixel (∼30 m) neighborhoods were the most informative metrics for forest classification in Corona images in the temperate and tropical study areas, respectively. The trajectory of change from the 1960s to 2000s differed between the two study areas: the average annual forest loss rate in the urban area doubled from 0.68% to 1.9% from the 1960s to the mid-1980s and then decreased during the following decade. In contrast, deforestation in the Brazilian study area continued at a slightly increased pace between the 1960s and 1990s at annual loss rate of 0.62–0.79% and quickly slowed down afterward. This study demonstrates the strong potential of declassified Corona images for detecting historical forest changes in these study regions and suggests increased utility for retrieving a wide range of land cover histories around the world. Numéro de notice : A2015-697 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.09.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.09.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78333
in ISPRS Journal of photogrammetry and remote sensing > vol 103 (May 2015) . - pp 81 - 92[article]L'approche détection des changements pour estimer l'humidité du sol en milieu semi-aride à partir d'images ASAR, cas des hautes plaines de l'Est de l'Algérie / Mokhtar Guerfi in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)PermalinkClassifying compound structures in satellite images : A compressed representation for fast queries / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkExtraction des éléments de façade de bâtiments du patrimoine architectural à partir de données issues de scanner laser terrestre / Kenza Aitelkadi in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)PermalinkFast subpixel mapping algorithms for subpixel resolution change detection / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkLinear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification / Keng-Hao Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkObject-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)PermalinkA physics-based unmixing method to estimate subpixel temperatures on mixed pixels / Manuel Cubero-Castan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkA technique for simultaneous visualization and segmentation of hyperspectral data / Abhimitra Meka in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkTraining set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery / Lei Ma in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)PermalinkContextual classification of point cloud data by exploiting individual 3d neigbourhoods / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)Permalink