Centre dedocumentation
scientifique

The weight matrix determination of systematic bias calibration for a laser altimeter / Ma Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 11 (November 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 11 (November 2016) . - pp 847 - 852 Titre : The weight matrix determination of systematic bias calibration for a laser altimeter Type de document : Article/Communication Auteurs : Ma Yue, Auteur ; Li Song, Auteur ; Lu Xiushan, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 847 - 852 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données IceSat-Glas [Termes descripteurs IGN] erreur de mesure [Termes descripteurs IGN] étalonnage [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] incertitude de mesurage [Termes descripteurs IGN] matrice [Termes descripteurs IGN] matrice d'erreur Résumé : (Auteur) The geolocation accuracy of satellite laser altimeters is significantly influenced by on-orbit misalignment and ranging biases. Few researchers have investigated the weight matrix determination method, which plays a critical role in bias estimation. In this article, a systematic misalignment and ranging bias model was deduced. Based on the least squares criterion, a bias calibration method was designed for use with solid natural surfaces; and the weight matrix was defined according to the ranging uncertainty theory. Referring to the Geoscience Laser Altimeter System (glas) parameters, the established model and method were verified using programming simulations, which indicated with a misalignment of tens of arc-seconds in the pitch and roll directions and a ranging bias of several centimeters, by using the weight matrix, the estimation accuracies of the misalignment and ranging bias increased by 0.22 and 2 cm, respectively. Consequently, the geolocation accuracy increased by approximately 0.64 m horizontally and 3 cm vertically for a 1° sloping surface. Numéro de notice : A2016-944 Thématique : POSITIONNEMENT Nature : Article En ligne : http://dx.doi.org/10.14358/PERS.82.11.847 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83436 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2016112	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
105-2016111	SL	Revue	Centre de documentation	Revues en salle	Disponible

A measure of average error variance of line features / Eryong Liu in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)  

Public [article]  inCartography and Geographic Information Science > Vol 43 n° 4 (September 2016) . - pp 321 - 327 Titre : A measure of average error variance of line features Type de document : Article/Communication Auteurs : Eryong Liu, Auteur ; Wenzhong Shi, Auteur Année de publication : 2016 Article en page(s) : pp 321 - 327 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] erreur de positionnement [Termes descripteurs IGN] erreur moyenne [Termes descripteurs IGN] interpolation cubique [Termes descripteurs IGN] interpolation linéaire [Termes descripteurs IGN] ligne caractéristique [Termes descripteurs IGN] matrice d'erreur [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] objet géographique linéaire [Termes descripteurs IGN] propagation d'erreur [Termes descripteurs IGN] qualité des données Résumé : (Auteur) This article presents a new development in measuring the positional error of line features in Geographic Information Systems (GIS), in the form of a new measure for estimating the average error variance of line features, including line segment, polyline, polygon, and curved lines. This average error measure is represented in the form of a covariance matrix derived by an analytical approach. Corresponding error indicators are derived from this matrix. The error of line features mainly results from two factors: (1) an error propagated from the original component points of line features and (2) a model error of interpolation between these points. In this study, a method of average error estimation has been derived regarding the first type error of line features that are interpolated by either linear or cubic interpolation methods. The main contribution of the research is the provision of an error measure to assess the quality of spatial data in application settings. The proposed error models for estimating average error variance of line features in a GIS are illustrated by both simulated and practical experiments. The results show that the line accuracy from a linear interpolation is better than a line interpolated using a cubic model. Numéro de notice : A2016-417 Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1080/15230406.2015.1077738 En ligne : https://doi.org/10.1080/15230406.2015.1077738 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81312 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2016041	SL	Revue	Centre de documentation	Revues en salle	Disponible

Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region / George P. Petropoulos in Geocarto international, vol 28 n° 1-2 (February - May 2013)  

Public [article]  inGeocarto international > vol 28 n° 1-2 (February - May 2013) . - pp 114 - 129 Titre : Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region Type de document : Article/Communication Auteurs : George P. Petropoulos, Auteur ; Krishna Prasad Vadrevu, Auteur ; Chariton Kalaitzidis, Auteur Année de publication : 2013 Article en page(s) : pp 114 - 129 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification Spectral angle mapper [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] littoral méditerranéen [Termes descripteurs IGN] matrice d'erreur [Termes descripteurs IGN] occupation du sol Résumé : (Auteur) In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting. Numéro de notice : A2013-278 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.668950 date de publication en ligne : 02/04/2012 En ligne : https://doi.org/10.1080/10106049.2012.668950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32416 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2013011	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Generalization-oriented road line classification by means of an artificial neural network / J.L. Garcia Balboa in Geoinformatica, vol 12 n° 3 (September - November 2008)

Public [article]  inGeoinformatica > vol 12 n° 3 (September - November 2008) . - pp 289 - 312 Titre : Generalization-oriented road line classification by means of an artificial neural network Type de document : Article/Communication Auteurs : J.L. Garcia Balboa, Auteur ; Francisco Javier Ariza-López, Auteur Année de publication : 2008 Article en page(s) : pp 289 - 312 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] acquisition de connaissances [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] axe médian [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] matrice d'erreur [Termes descripteurs IGN] objet géographique linéaire [Termes descripteurs IGN] route [Termes descripteurs IGN] segmentation [Vedettes matières IGN] Généralisation Résumé : (Auteur) In line generalization, a first goal to achieve is the classification of features previous to the selection of processes and parameters. A feed forward backpropagation artificial neural network (ANN) is designed for classifying a set of road lines through a supervised learning process, attempting to emulate a classification performed by a human expert for cartographic generalization purposes. The main steps of the process are presented in this paper: (a) experimental data selection; (b) segmentation of lines into homogeneous sections, (c) sections enrichment through a set of quantitative measures derived from a principal component analysis, and qualitative information derived from road network and road type; (d) expert classification of the sections; and finally (e) the ANN design, training and validation. The quality of results is analyzed by means of error matrices after a cross-validation process giving a goodness, or percentage of agreement, over 83%. Copyright Springer Numéro de notice : A2008-282 Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29275 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-08031	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Comparing accuracy assessments to infer superiority of image classification methods / J. De Leleuw in International Journal of Remote Sensing IJRS, vol 27 n°1-2 (January 2006)

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n°1-2 (January 2006) . - pp 223 - 232 Titre : Comparing accuracy assessments to infer superiority of image classification methods Type de document : Article/Communication Auteurs : J. De Leleuw, Auteur ; H. Jia, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 223 - 232 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] classification [Termes descripteurs IGN] flore halophile [Termes descripteurs IGN] fonction aléatoire [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] Kappa de Cohen [Termes descripteurs IGN] marais salé [Termes descripteurs IGN] matrice d'erreur [Termes descripteurs IGN] Pays-Bas [Termes descripteurs IGN] variance Résumé : (Auteur) The z-test based on the Kappa statistic is commonly used to infer superiority of one map production method over another. Typically the same reference data set is used to calculate and next compare the Kappa's of the two maps. This data structure easily leads to dependence between the two error-matrices. This may result in overly large variance estimates and too conservative inference about the difference in accuracy between the two methods. Tests considering the dependency between the error matrices would be more sensitive in such case. In this article we compare the performance of two such tests, a randomization and McNemar's test, with the traditional z-test. We compared 16 alternative methods to classify salt marsh vegetation in The Netherlands. The error matrices were positively associated in all 120 possible comparisons of pairs of classification methods. This suggests that dependency between pairs of error matrices used in classifier comparison is a common phenomenon. Both the randomization and McNemar test gave lower p values and rejecied the null hypothesis of equal performance more frequently than the z-test. We therefore recommend considering their use. Numéro de notice : A2006-060 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27787 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-06011	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data / Russell G. Congalton in Photogrammetric Engineering & Remote Sensing, PERS, vol 54 n° 5 (may 1988)

Permalink

Using classification error matrices to improve the accuracy of weighted land-cover models / S.P. Prisley in Photogrammetric Engineering & Remote Sensing, PERS, vol 53 n° 9 (september 1987)

Permalink