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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

Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) / Chang Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1837 - 1859 Titre : Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) Type de document : Article/Communication Auteurs : Chang Li, Auteur ; Sisi Zhao, Auteur ; Qing Wang, Auteur ; Wenzhong Shi, Auteur Année de publication : 2018 Article en page(s) : pp 1837 - 1859 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] autocorrélation spatiale [Termes IGN] incertitude des données [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle numérique de surface [Termes IGN] propagation d'erreur Résumé : (Auteur) In the field of digital terrain analysis (DTA), the principle and method of uncertainty in surface area calculation (SAC) have not been deeply developed and need to be further studied. This paper considers the uncertainty of data sources from the digital elevation model (DEM) and SAC in DTA to perform the following investigations: (a) truncation error (TE) modeling and analysis, (b) modeling and analysis of SAC propagation error (PE) by using Monte-Carlo simulation techniques and spatial autocorrelation error to simulate DEM uncertainty. The simulation experiments show that (a) without the introduction of the DEM error, higher DEM resolution and lower terrain complexity lead to smaller TE and absolute error (AE); (b) with the introduction of the DEM error, the DEM resolution and terrain complexity influence the AE and standard deviation (SD) of the SAC, but the trends by which the two values change may be not consistent; and (c) the spatial distribution of the introduced random error determines the size and degree of the deviation between the calculated result and the true value of the surface area. This study provides insights regarding the principle and method of uncertainty in SACs in geographic information science (GIScience) and provides guidance to quantify SAC uncertainty. Numéro de notice : A2018-305 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1469136 Date de publication en ligne : 04/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1469136 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90448 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2018051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A Geometric and Radiometric Simultaneous Correction Model (GRSCM) framework for high-accuracy remotely sensed image preprocessing / Chang Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 9 (September 2017) . - pp 621 - 632 Titre : A Geometric and Radiometric Simultaneous Correction Model (GRSCM) framework for high-accuracy remotely sensed image preprocessing Type de document : Article/Communication Auteurs : Chang Li, Auteur ; Hao Xiong, Auteur Année de publication : 2017 Article en page(s) : pp 621 - 632 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] correction d'image [Termes IGN] correction géométrique [Termes IGN] correction radiométrique [Termes IGN] Ransac (algorithme) [Termes IGN] restauration d'image Résumé : (Auteur) The grey value g (x, y) of pixel on radiometric spectrum is regarded as a function of the geometric coordinates (x, y). Hence, there is a unity of opposite relationships between the geometric and radiometric information, such that, these two types of information cannot be separated. Therefore, this paper proposes a novel geometric and radiometric simultaneous correction model (GRSCM) framework inspired and developed from least squares matching (LSM). Based on the Gauss-Markov model, geometric and radiometric correction coefficients are integrated and solved by an iterative method with variable weights in the proposed model. Moreover, many state-of-theart models and methods can be integrated into the proposed general GRSCM framework. In the GRSCM of this paper, RANdom SAmple Consensus (RANSAC), stepwise regression and significance testing are integrated and used. The experimental results demonstrate that the accuracy of the GRSCM is significantly improved compared with that of geometric correction and radiometric correction separately. Numéro de notice : A2017-608 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.9.621 En ligne : https://doi.org/10.14358/PERS.83.9.621 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86886 [article]

A Stepwise-Then-Orthogonal Regression (STOR) with quality control for optimizing the RFM of high-resolution satellite imagery / Chang Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 9 (September 2017) . - pp 611 - 620 Titre : A Stepwise-Then-Orthogonal Regression (STOR) with quality control for optimizing the RFM of high-resolution satellite imagery Type de document : Article/Communication Auteurs : Chang Li, Auteur ; Xiaojuan Liu, Auteur ; Yongjun Zhang, Auteur ; Zuxun Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 611 - 620 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] contrôle qualité [Termes IGN] détection d'erreur [Termes IGN] erreur aléatoire [Termes IGN] image à haute résolution [Termes IGN] image SPOT 5 [Termes IGN] modèle par fonctions rationnelles [Termes IGN] régression Résumé : (auteur) There are two major problems in Rational Function Model (RFM) solution: (a) Data source error, including gross error, random error, and systematic error; and (b) Model error, including over-parameterization and over-correction issues caused by unnecessary RFM parameters and exaggeration of random error in constant term of error-in-variables (EIV) model, respectively. In order to solve two major problems simultaneously, we propose a new approach named stepwise-then-orthogonal regression (STOR) with quality control. First, RFM parameters are selected by stepwise regression with gross error detection. Second, the revised orthogonal distance regression is utilized to adjust random error and address the overcorrection problem. Third, systematic error is compensated by Fourier series. The performance of conventional strategies and the proposed STOR are evaluated by control and check grids generated from SPOT5 high-resolution imagery. Compared with the least squares regression, partial least squares regression, ridge regression, and stepwise regression, the proposed STOR shows a significant improvement in accuracy. Numéro de notice : A2017-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.9.611 En ligne : https://doi.org/10.14358/PERS.83.9.611 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86874 [article]

Robust sparse hyperspectral unmixing with ℓ2,1 norm / Yong Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1227 - 1239 Titre : Robust sparse hyperspectral unmixing with ℓ2,1 norm Type de document : Article/Communication Auteurs : Yong Ma, Auteur ; Chang Li, Auteur ; Xiaoguang Mei, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1227 - 1239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] image hyperspectrale [Termes IGN] matrice creuse [Termes IGN] méthode robuste [Termes IGN] pondération Résumé : (Auteur) Sparse unmixing (SU) of hyperspectral data have recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model, which ignores the possible nonlinear effects (i.e., nonlinearity), and the nonlinearity is merely treated as outlier. Besides, the traditional SU algorithms often adopt the ℓ2 norm loss function, which makes them sensitive to noises and outliers. In this paper, we propose a robust SU (RSU) method with ℓ2,1 norm loss function, which is robust for noises and outliers. Then, the RSU can be solved by the alternative direction method of multipliers. Finally, the experiments on both synthetic data sets and real hyperspectral images demonstrate that the proposed RSU is efficient for solving the hyperspectral SU problem compared with the state-of-the-art algorithms. Numéro de notice : A2017-150 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2616161 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2616161 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84681 [article]

Automatic keyline recognition and 3D reconstruction for quasi-planar façades in close-range images / Chang Li in Photogrammetric record, vol 31 n° 153 (March - May 2016)  

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