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Use intermediate results of wrapper band selection methods: A first step toward the optimization of spectral configuration for land cover classifications / Arnaud Le Bris (2014)
Titre : Use intermediate results of wrapper band selection methods: A first step toward the optimization of spectral configuration for land cover classifications Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur ; Xavier Briottet , Auteur ; Nicolas Paparoditis , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2014 Conférence : WHISPERS 2014, 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing 24/06/2014 27/06/2014 Lausanne Suisse Proceedings IEEE Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] zone urbaineRésumé : (auteur) Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) associated to a classifier (linear SVM) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. The impact of the number of selected bands on classification accuracy was obtained thanks to SFFS, while a band importance measure was derived from intermediate sets of bands tested by GA. Such results are a first step toward the identification of the most suitable spectral bands to design superspectral camera systems dedicated to specific applications (e.g. classification of urban land cover and material maps). Numéro de notice : C2014-042 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/WHISPERS.2014.8077653 Date de publication en ligne : 26/10/2017 En ligne : https://doi.org/10.1109/WHISPERS.2014.8077653 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99587 Documents numériques
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Use intermediate results ... - pdf auteurAdobe Acrobat PDF Large-scale classification of water areas using airborne topographic lidar data / Julien Smeeckaert in Remote sensing of environment, vol 138 (November 2013)
[article]
Titre : Large-scale classification of water areas using airborne topographic lidar data Type de document : Article/Communication Auteurs : Julien Smeeckaert, Auteur ; Clément Mallet , Auteur ; Nicolas David , Auteur ; Nesrine Chehata , Auteur ; António Ferraz , Auteur Année de publication : 2013 Article en page(s) : pp 134 - 148 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] grande échelle
[Termes IGN] littoral
[Termes IGN] modèle numérique de terrain
[Termes IGN] rive
[Termes IGN] rivière
[Termes IGN] semis de points
[Termes IGN] trait de côteRésumé : (auteur) Accurate Digital Terrain Models (DTMs) are inevitable inputs for mapping and analyzing areas subject to natural hazards. Topographic airborne laser scanning has become an established technique to characterize the Earth's surface: lidar provides 3D point clouds allowing for a fine reconstruction of the topography while preserving high frequencies of the relief. For flood hazard modeling, the key step, before going onto terrain modeling, is the discrimination of land and water areas within the delivered point clouds. Therefore, instantaneous shorelines, river banks, and inland waters can be extracted as a basis for more reliable DTM generation. This paper presents an automatic, efficient, and versatile workflow for land/water classification of airborne topographic lidar points, effective at large scales (>300 km2). For that purpose, the Support Vector Machine (SVM) method is used as a classification framework and it is embedded in a workflow designed for our specific goal. First, a restricted but carefully designed set of features, based only on 3D lidar point coordinates and flightline information, is defined as classifier input. Then, the SVM learning step is performed on small but well-targeted areas thanks to a semiautomatic region growing strategy. Finally, label probability output by SVM is merged with contextual knowledge during a probabilistic relaxation step in order to remove pixel-wise misclassification. Results show that a survey of hundreds of millions of points are labeled with high accuracy (>95% in most cases for coastal areas, and >90% for rivers) and that small natural and anthropic features of interest are still well classified even though we work at lowpoint densities (0.5–4 pts/m2). We also noticed that it may fail in water-logged areas. Nevertheless, our approach remains valid for regional and national mapping purposes, coasts and rivers, and provides a strong basis for further discrimination of land-cover classes and coastal habitats. Numéro de notice : A2013-792 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2013.07.004 Date de publication en ligne : 15/08/2013 En ligne : https://doi.org/10.1016/j.rse.2013.07.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80174
in Remote sensing of environment > vol 138 (November 2013) . - pp 134 - 148[article]Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey / Ugur Alganci in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)
[article]
Titre : Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey Type de document : Article/Communication Auteurs : Ugur Alganci, Auteur ; Elif Sertel, Auteur ; Mutlu Ozdogan, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 1053 - 1065 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte agricole
[Termes IGN] classification orientée objet
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification Spectral angle mapper
[Termes IGN] cultures
[Termes IGN] image Landsat-TM
[Termes IGN] image SPOT 5
[Termes IGN] occupation du sol
[Termes IGN] parcelle agricole
[Termes IGN] photo-interprétation assistée par ordinateur
[Termes IGN] TurquieRésumé : (Auteur) This research investigates the accuracy of pixel- and object-based classification techniques across varying spatial resolutions to identify crop types at parcel level and estimate the area at six test sites to find the optimum data source for the identification of crop parcels. Multi-sensor data with spatial resolutions of 2.5 m, 5 m and 10 m from SPOT5 and 30 m from Landsat-5 TM were used. Maximum Likelihood (ML), Spectral Angle Mapper (SAM), and Support Vector Machines (SVM) were used as pixel-based methods in addition to object-based image classification (OBC). Post-classification methods were applied to the output of pixel-based classification to minimize the noise effects and heterogeneity within the agricultural parcels. In addition, processing-time performance of the algorithms was evaluated for the test sites and district scale classification. OBC results provided comparatively the best performance for both parcel identification and area estimation at 10 m and finer spatial resolution levels. SVM followed OBC at 2.5 m and 5 m resolutions but accuracies decreased dramatically with coarser resolutions. ML and SAM results were worse up to 30 m resolution for both crop type identification and area estimation. In general, parcel identification efficiency was strongly correlated with spatial resolution while the classification algorithm was a more effective factor than spatial resolution for area estimation accuracy. Results also provided an opportunity to discuss the effects of image resolution and the classification algorithm independent factors such as parcel size, spatial distribution of crop types and crop patterns. Numéro de notice : A2013-599 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.1053 En ligne : https://doi.org/10.14358/PERS.79.11.1053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32735
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 1053 - 1065[article]Information content of very high resolution SAR images: study of feature extraction and imaging parameters / Corneliu Dimitru in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)
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Titre : Information content of very high resolution SAR images: study of feature extraction and imaging parameters Type de document : Article/Communication Auteurs : Corneliu Dimitru, Auteur ; Mihai Datcu, Auteur Année de publication : 2013 Article en page(s) : pp 4591 - 4610 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] angle d'incidence
[Termes IGN] Berlin
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image radar moirée
[Termes IGN] image TerraSAR-X
[Termes IGN] matrice de co-occurrence
[Termes IGN] orbite
[Termes IGN] Ottawa
[Termes IGN] Toulouse
[Termes IGN] transformation de Fourier
[Termes IGN] VeniseRésumé : (Auteur) In this paper, we propose to study the dependence of information extraction technique performance on synthetic aperture radar (SAR) imaging parameters and the selected primitive features (PFs). The evaluation is done on TerraSAR-X data, and the interpretation is realized automatically. In the first part of this paper (use case I), the following issues are analyzed: 1) finding the optimal TerraSAR-X products and their limits of variability and 2) retrieving the number of categories/classes that can be extracted from the TerraSAR-X images using the PFs (gray-level co-occurrence matrix, Gabor filters, quadrature mirror filters, and nonlinear short-time Fourier transform). In the second part of this paper (use case II), we investigate the invariance of the products with the orbit direction and incidence angle. On the one hand, the results show that using ascending looking is better than using descending looking with an average accuracy increase of 7%-8%, approximately. On the other hand, the classification accuracy for the incidence angle varies from a lower value of the incidence to an upper value of the incidence angle (depending on the sensor range) with 4%-5%. The test sites are Venice (Italy), Toulouse (France), Berlin (Germany), and Ottawa (Canada) and are covering as much as possible the huge diversity of modes, types, and geometric resolution configuration of the TerraSAR-X. For the evaluation of all these parameters (resolution, features, orbit looking, and incidence angle), the support-vector-machine classifier is considered. To evaluate the accuracy of the classification, the precision/recall metric is calculated. The first contribution of this paper is the evaluation of different PFs (proposed in the literature for different types of images) and adaptation of these for SAR images. These features are compared (based on the accuracy of the classification) for the first time for a multiresolution pyramid specially built for this purpose. During the evaluation,- all the classes were annotated, and a semantic meaning was defined for each class. The second main contribution of this paper is the evaluation of the dependence on the patch size, orbit direction, and incidence angle of the TerraSAR-X. This type of evaluation has not been systematically investigated so far. For the evaluation of the optimal patch, two different patch sizes were defined, with the constrained that the size on ground needs to cover a minimum of one object (e.g., 200 * 200 m on ground). This patch size depends also on the parameters of the data such as resolution and pixel spacing. The investigation of orbit looking and incidence angle is very important for indexing large data sets that has a higher variability of these two parameters. These parameters influence the accuracy of the classification (e.g., if the incidence angle is closer to the lower bounds or closer to the upper bound of the satellite sensor range). Numéro de notice : A2013-423 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2265413 En ligne : https://doi.org/10.1109/TGRS.2013.2265413 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32561
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 8 (August 2013) . - pp 4591 - 4610[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013081 RAB Revue Centre de documentation En réserve L003 Disponible Texture classification of PolSAR data based on sparse coding of wavelet polarization textons / Chu He in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)
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Titre : Texture classification of PolSAR data based on sparse coding of wavelet polarization textons Type de document : Article/Communication Auteurs : Chu He, Auteur ; Shuang Li, Auteur ; Zixian Liao, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4576 - 4590 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image radar moirée
[Termes IGN] ondelette
[Termes IGN] polarimétrie radar
[Termes IGN] polarisation
[Termes IGN] texture d'imageRésumé : (Auteur) This paper presents a frame for classifying polarimetric synthetic aperture radar (PolSAR) data. The frame is based on the combination of wavelet polarization information, textons, and sparse coding. Polarimetric synthesis unites with the discrete wavelet frame to obtain wavelet polarization variance through the calculation of the wavelet variance in the space of polarization states. The K-means cluster algorithm is implemented to cluster the wavelet polarization variance vectors of the training samples for the purpose of constructing a texton dictionary. A patch, in which all the wavelet polarization variance vectors match those in the texton dictionary, is used to obtain a statistical histogram. Sparse coding is applied to describe the histogram feature and generate a new texture feature called sparse coding of a wavelet polarization texton. Finally, support vector machine is used for the classification. All experiments are carried out on five sets of PolSAR data. The experimental results confirm that the proposed method effectively classifies PolSAR data. Numéro de notice : A2013-422 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2236338 En ligne : https://doi.org/10.1109/TGRS.2012.2236338 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32560
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 8 (August 2013) . - pp 4576 - 4590[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013081 RAB Revue Centre de documentation En réserve L003 Disponible Semisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkA shape-based segmentation method for mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)PermalinkSpectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkBand grouping versus band clustering in SVM ensemble classification of hyperspectral imagery / Behnaz Bigdeli in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 6 (June 2013)PermalinkLearning with transductive SVM for semisupervised pixel classification of remote sensing imagery / Ujjwal Maulik in ISPRS Journal of photogrammetry and remote sensing, vol 77 (March 2013)PermalinkA graph-based classification method for hyperspectral images / J. Bai in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkModel driven reconstruction of roofs from sparse LIDAR point clouds / A. Henn in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)PermalinkSupport vector machine for spatial variation / C. Andris in Transactions in GIS, vol 17 n° 1 (February 2013)PermalinkPermalinkPermalink