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Auteur Liangpei Zhang |
Documents disponibles écrits par cet auteur (27)
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Fusion of multi-scale DEMs using a regularized super-resolution method / Linwei Yue in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)
[article]
Titre : Fusion of multi-scale DEMs using a regularized super-resolution method Type de document : Article/Communication Auteurs : Linwei Yue, Auteur ; Huanfeng Shen, Auteur ; Qiangqiang Yuan, Auteur ; Liangpei Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 2095 - 2120 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] couverture (données géographiques)
[Termes IGN] données multisources
[Termes IGN] modèle numérique de terrain
[Termes IGN] représentation multipleRésumé : (Auteur) The digital elevation model (DEM) is a significant digital representation of a terrain surface. Although a variety of DEM products are available, they often suffer from problems varying in spatial coverage, data resolution, and accuracy. However, the multi-source DEMs often contain supplementary information, which makes it possible to produce a higher-quality DEM through blending the multi-scale data. Inspired by super-resolution (SR) methods, we propose a regularized framework for the production of high-resolution (HR) DEM data with extended coverage. To deal with the registration error and the horizontal displacement among multi-scale measurements, robust data fidelity with weighted norm is employed to measure the conformance of the reconstructed HR data to the observed data. Furthermore, a slope-based Markov random field (MRF) regularization is used as the spatial regularization. The proposed method can simultaneously handle complex terrain features, noises, and data voids. Using the proposed method, we can reconstruct a seamless DEM data with the highest resolution among the input data, and an extensive spatial coverage. The experiments confirmed the effectiveness of the proposed method under different cases. Numéro de notice : A2015-620 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1063639 En ligne : https://doi.org/10.1080/13658816.2015.1063639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78089
in International journal of geographical information science IJGIS > vol 29 n° 12 (December 2015) . - pp 2095 - 2120[article]A moving weighted harmonic analysis method for reconstructing high-quality SPOT VEGETATION NDVI time-series data / Gang Yang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 11 (November 2015)
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Titre : A moving weighted harmonic analysis method for reconstructing high-quality SPOT VEGETATION NDVI time-series data Type de document : Article/Communication Auteurs : Gang Yang, Auteur ; Huanfeng Shen, Auteur ; Liangpei Zhang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 6008 - 6021 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] analyse harmonique
[Termes IGN] Chine
[Termes IGN] filtrage numérique d'image
[Termes IGN] image SPOT-Végétation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (Auteur) Global or regional environmental change is of wide concern. Extensive studies have indicated that long-term vegetation cover change is one of the most important factors reflecting environmental change, and normalized difference vegetation index (NDVI) time-series data sets have been widely used in vegetation dynamic change monitoring. However, the significant residual effects and noise levels impede the application of NDVI time-series data in environmental change research. This study develops a novel and robust filter method, i.e., the moving weighted harmonic analysis (MWHA) method, which incorporates a moving support domain to assign the weights for all the points, making the determination of the frequency number much easier. Additionally, a four-step process flow is designed to make the data approach the upper NDVI envelope, so that the actual change in the vegetation can be detected. A total of 487 test pixels selected from SPOT VEGETATION 10-day MVC NDVI time-series data from January 1999 to December 2001 were used to illustrate the effectiveness of the new method by comparing the MWHA results with the results of another four existing methods. Finally, the long-term SPOT VEGETATION 10-day maximum-value compositing (MVC) NDVI time series for China from April 1998 to May 2014 was reconstructed by the use of the proposed method, and a test region in China was utilized to validate the effectiveness of the proposed MWHA method. All the results indicate that the reconstructed high-quality NDVI time series fits the actual growth profile of the vegetation and is suitable for use in further remote sensing applications. Numéro de notice : A2015-771 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2431315 Date de publication en ligne : 02/06/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2431315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78827
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 11 (November 2015) . - pp 6008 - 6021[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015111 SL Revue Centre de documentation Revues en salle Disponible Efficient superpixel-level multitask joint sparse representation for hyperspectral image classification / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)
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Titre : Efficient superpixel-level multitask joint sparse representation for hyperspectral image classification Type de document : Article/Communication Auteurs : Jiayi Li, Auteur ; Hongyan Zhang, Auteur ; Liangpei Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 5338 - 5351 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse infrapixellaire
[Termes IGN] apprentissage automatique
[Termes IGN] classification
[Termes IGN] données clairsemées
[Termes IGN] image hyperspectrale
[Termes IGN] représentation des donnéesRésumé : (Auteur) In this paper, we propose a superpixel-level sparse representation classification framework with multitask learning for hyperspectral imagery. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and the correlation and distinctiveness of pixels in a spatial local region. Compared with some of the state-of-the-art hyperspectral classifiers, the superiority of the multiple-feature combination, the spatial prior utilization, and the computational complexity are maintained at the same time in the proposed method. The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparse (collaborative) representation-based algorithms and some popular hyperspectral multiple-feature classifiers. Numéro de notice : A2015-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421638 Date de publication en ligne : 29/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78758
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5338 - 5351[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015101 SL Revue Centre de documentation Revues en salle Disponible An abundance characteristic-based independent component analysis for hyperspectral unmixing / Nan Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
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Titre : An abundance characteristic-based independent component analysis for hyperspectral unmixing Type de document : Article/Communication Auteurs : Nan Wang, Auteur ; Liangpei Zhang, Auteur ; Lifu Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 416 - 428 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] image hyperspectraleRésumé : (Auteur) Independent component analysis (ICA) has been recently applied into hyperspectral unmixing as a result of its low computation time and its ability to perform without prior information. However, when applying ICA for hyperspectral unmixing, the independence assumption in the ICA model conflicts with the abundance sum-to-one constraint and the abundance nonnegative constraint in the linear mixture model, which affects the hyperspectral unmixing accuracy. In this paper, we consider an abundance matrix composed of Np-dimensional variables, and we propose a new hyperspectral unmixing approach with an abundance characteristic-based ICA model. Two characteristics of the abundance variables are explored, and the model is constructed by these characteristics. A corresponding gradient descent algorithm is also proposed to solve the proposed objective function. Both the synthetic and real experimental results demonstrate that the proposed method performs better than the other state-of-the-art methods in abundance and endmember extraction. Numéro de notice : A2015-034 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2322862 En ligne : https://doi.org/10.1109/TGRS.2014.2322862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75116
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 416 - 428[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015011 RAB Revue Centre de documentation En réserve L003 Disponible Adaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery / Ruyi Feng in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)
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Titre : Adaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery Type de document : Article/Communication Auteurs : Ruyi Feng, Auteur ; Yanfei Zhong, Auteur ; Liangpei Zhang, Auteur Année de publication : 2014 Article en page(s) : pp 9 – 24 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] traitement d'imageRésumé : (Auteur) Sparse unmixing models based on sparse representation theory and a sparse regression model have been successfully applied to hyperspectral remote sensing image unmixing. To better utilize the abundant spatial information and improve the unmixing accuracy, spatial sparse unmixing methods such as the non-local sparse unmixing (NLSU) approach have been proposed. Although the NLSU method utilizes non-local spatial information as the spatial regularization term and obtains a satisfactory unmixing accuracy, the final abundances are affected by the non-local neighborhoods and drift away from the true abundance values when the observed hyperspectral images have high noise levels. Furthermore, NLSU contains two regularization parameters which need to be appropriately set in real applications, which is a difficult task and often has a high computational cost. To solve these problems, an adaptive non-local Euclidean medians sparse unmixing (ANLEMSU) method is proposed to improve NLSU by replacing the non-local means total variation spatial consideration with the non-local Euclidean medians filtering approach. In addition, ANLEMSU utilizes a joint maximum a posteriori (JMAP) strategy to acquire the relationships between the regularization parameters and the estimated abundances, and achieves the fractional abundances adaptively, without the need to set the two regularization parameters manually. The experimental results using both simulated data and real hyperspectral images indicate that ANLEMSU outperforms the previous sparse unmixing algorithms and, hence, provides an effective option for the unmixing of hyperspectral remote sensing imagery. Numéro de notice : A2014-522 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.07.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.07.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74134
in ISPRS Journal of photogrammetry and remote sensing > vol 97 (November 2014) . - pp 9 – 24[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014111 RAB Revue Centre de documentation En réserve L003 Disponible Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery / Yanfei Zhong in ISPRS Journal of photogrammetry and remote sensing, vol 96 (October 2014)PermalinkHyperspectral remote sensing image subpixel target detection based on supervised metric learning / Lefei Zhang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)PermalinkCloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model / Qing Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 92 (June 2014)PermalinkHyperspectral image denoising with a spatial–spectral view fusion strategy / Qiangqiang Yuan in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)PermalinkSlow feature analysis for change detection in multispectral imagery / Chen Wu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)PermalinkAdaptive subpixel mapping based on a multiagent system for remote-sensing imagery / Xiong Xu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)PermalinkMultiagent object-based classifier for high spatial resolution imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)Permalink