Centre de documentation
scientifique

Dépouillements

An image-pyramid-based raster-to-vector conversion (IPBRTVC) framework for consecutive-scale cartography and synchronized generalization of classic objects / Chang Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 3 (March 2019) . - pp 169 - 178 Titre : An image-pyramid-based raster-to-vector conversion (IPBRTVC) framework for consecutive-scale cartography and synchronized generalization of classic objects Type de document : Article/Communication Auteurs : Chang Li, Auteur ; Xiaojuan Liu, Auteur ; Lu Wei, Auteur Année de publication : 2019 Article en page(s) : pp 169 - 178 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] chaîne de traitement [Termes IGN] contrôle qualité [Termes IGN] détection d'objet [Termes IGN] image DMSP-OLS [Termes IGN] image Landsat-8 [Termes IGN] vectorisation Résumé : (Auteur) There are some key problems in raster-to-vector conversion and cartographic generalization, which include (1) deficient automation and low accuracy in the traditional raster-to-vector conversion processing; (2) data-source inconsistency in cartographic generation, i.e., different raster data sources converted to vector; and (3) how to acquire arbitrary-scale vector data. To solve these problems, we initially propose an innovative image-pyramid-based raster-to-vector conversion (IPBRTVC) framework with quality control for consecutive-scale cartography and synchronized generalization, of which details can be modified accordingly under the IPBRTVC framework. Landsat-8 imagery and Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) night-time light imagery are used as a test dataset to extract classic objects in the geometry level. Experimental results show that the IPBRTVC framework not only solves the aforementioned problems well but also (1) improves efficiency of data processing by avoiding problems of corresponding features matching and topology errors, (2) contributes to develop relevant parallel computing system, and (3) helps to integrate the raster-to-vector conversion and consecutive-scale cartography. Numéro de notice : A2019-146 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.3.169 Date de publication en ligne : 01/03/2019 En ligne : https://doi.org/10.14358/PERS.85.3.169 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92474 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019031	SL	Revue	Centre de documentation	Revues en salle	Disponible

Land cover classification in combined elevation and optical images supported by OSM data, mixed-level features, and non-local optimization algorithms / Dimitri Bulatov in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 3 (March 2019) . - pp 179 - 195 Titre : Land cover classification in combined elevation and optical images supported by OSM data, mixed-level features, and non-local optimization algorithms Type de document : Article/Communication Auteurs : Dimitri Bulatov, Auteur ; Gisela Häufel, Auteur ; Lucas Lucks, Auteur ; Melanie Pohl, Auteur Année de publication : 2019 Article en page(s) : pp 179 - 195 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] champ aléatoire de Markov [Termes IGN] classification dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données localisées des bénévoles [Termes IGN] extraction automatique [Termes IGN] milieu urbain [Termes IGN] OpenStreetMap [Termes IGN] orthoimage Résumé : (Auteur) Land cover classification from airborne data is considered a challenging task in Remote Sensing. Even in the case of available elevation data, shadows and strong intra-class variations of appearances are abundant in urban terrain. In this paper, we propose an approach for supervised land cover classification that has three main contributions. Firstly, for the cumbersome task of training data sampling we propose an algorithm which combines the freely available OpenStreetMap data with the actual sensor data and requires only a minimum of user interaction. The key idea of this algorithm is to rasterize the vector data using a fast segmentation result. Secondly, pixel-wise classification may take long and be quite sensitive to the resolution and quality of input data. Therefore, superpixel decomposition of images, supported by a general framework on operations with superpixels, guarantees fast grouping of pixel-wise features and their assignment to one of four important classes (building, tree, grass and road). Particularly for extraction of street canyons lying in the shadowy regions, high-level features based on stripes are introduced. Finally, the output of a probabilistic learning algorithm can be postprocessed by a non-local optimization module operating on Markov Random Fields, thus allowing to correct noisy results using a smoothness prior. Extensive tests on three datasets of quite different nature have been performed with two probabilistic learners: The well-known Random Forest and by far less known Import Vector Machine are explored. Thus, this work provides insights about promising feature sets for both classifiers. The quantitative results for the ISPRS benchmark dataset Vaihingen are promising, achieving up to 94.5% and 87.1% accuracy on superpixel and on pixel level, respectively, despite the fact that only around 10% of available labeled data were used. At the same time, the results for two additional datasets, validated with the autonomously acquired training data, yielded a significantly lower number of misclassified superpixels. This confirms that the proposed algorithm on training data extraction works quite well in reducing errors of second kind. However, it tends to extract predominantly huge and easy-to-classify areas, while in complicated, ambiguous regions, first type errors often occur. For this and other algorithm shortcomings, directions of future research are outlined. Numéro de notice : A2019-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.3.179 Date de publication en ligne : 01/03/2019 En ligne : https://doi.org/10.14358/PERS.85.3.179 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92476 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019031	SL	Revue	Centre de documentation	Revues en salle	Disponible

Improved camera distortion correction and depth estimation for lenslet light field camera / Changkun Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 3 (March 2019) . - pp 197 - 208 Titre : Improved camera distortion correction and depth estimation for lenslet light field camera Type de document : Article/Communication Auteurs : Changkun Yang, Auteur ; Zhaoqin Liu, Auteur ; Kaichang Di, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 197 - 208 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] caméra numérique [Termes IGN] carte de profondeur [Termes IGN] correction géométrique [Termes IGN] distorsion d'image [Termes IGN] modèle géométrique de prise de vue Résumé : (Auteur) A light field camera can capture both radiance and angular information, providing a novel solution for depth estimation. The paper proposes two improved methods including distortion model optimization and depth estimation refinement for a lenslet light field camera. For distortion model optimization, a novel 14-parameter distortion model that involves sub-aperture images generation is applied to correct the light field camera images. For depth estimation refinement, an algorithm reducing the high influence of outliers on depth estimation in weak texture regions is proposed based on multi-view stereo matching using the cost volume. Experimental results show the projection error has decreased by about 30% and the depth root-mean-squared error on real world images has decreased by about 42% with our distortion correction method and depth estimation method compared with state of art algorithms. It verifies the correctness and effectiveness of our proposed methods and show significant improvement on accuracy of depth map estimation. Numéro de notice : A2019-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.3.197 Date de publication en ligne : 01/03/2019 En ligne : https://doi.org/10.14358/PERS.85.3.197 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92477 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019031	SL	Revue	Centre de documentation	Revues en salle	Disponible

An evaluation of reflectance calibration methods for UAV spectral imagery / Jarrod Edwards in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 3 (March 2019) . - pp 221 - 230 Titre : An evaluation of reflectance calibration methods for UAV spectral imagery Type de document : Article/Communication Auteurs : Jarrod Edwards, Auteur ; John Anderson, Auteur ; William Shuart, Auteur ; Jason Woolard, Auteur Année de publication : 2019 Article en page(s) : pp 221 - 230 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] capteur multibande [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] étalonnage radiométrique [Termes IGN] image captée par drone [Termes IGN] méthode empirique de ligne [Termes IGN] orthophotoplan numérique [Termes IGN] Pix4D [Termes IGN] réflectance spectrale [Termes IGN] régression Résumé : (Auteur) Spectral imagery using micro-unmanned aerial vehicles is rapidly advancing. This study compared reflectance calibration methods for imagery acquired using the Parrot Sequoia imager, a commercial multispectral sensor package. For the study, two orthomosaics were calibrated using 1) a manufacturer-suggested AIRINOV standard correction using PIX4D software and 2) the Empirical Line Calibration (ELC) method using ground radiometric data on specific in-scene targets. Both scenes were analyzed for target spectral agreement by ground radiometric survey. Regression analysis demonstrated more favorable target correlation for the ELC imagery than the AIRINOV-calibrated imagery, with Root Mean Square Error (RMSE) analysis supporting these results. Finally, classification maps were produced between the data sets. Error analysis resulted in an overall accuracy of 24% for the AIRINOV map compared to ELC-based truth data, with a considerable number of pixels associated with brighter targets unclassified. These results demonstrate the need for standardized calibration procedures in the spectral correction of small-format remote sensor data. Numéro de notice : A2019-149 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.3.221 Date de publication en ligne : 01/03/2019 En ligne : https://doi.org/10.14358/PERS.85.3.221 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92502 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019031	SL	Revue	Centre de documentation	Revues en salle	Disponible