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Auteur Jie Li |
Documents disponibles écrits par cet auteur (6)
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Adaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)
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Titre : Adaptive edge preserving maps in Markov random fields for hyperspectral image classification Type de document : Article/Communication Auteurs : Chao Pan, Auteur ; Xiuping Jia, Auteur ; Jie Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8568 - 8583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] accentuation de contours
[Termes IGN] algorithme Graph-Cut
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classe d'objets
[Termes IGN] détection de contours
[Termes IGN] étiquette de classe
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] segmentation d'imageRésumé : (auteur) This article presents a novel adaptive edge preserving (aEP) scheme in Markov random fields (MRFs) for hyperspectral image (HSI) classification. MRF regularization usually suffered from over-smoothing at boundaries and insufficient refinement within class objects. This work divides and conquers this problem class-by-class, and integrates K ( K−1 )/2 ( K is the class number) aEP maps (aEPMs) in MRF model. Spatial label dependence measure (SLDM) is designed to estimate the interpixel label dependence for given spectral similarity measure. For each class pair, aEPM is optimized by maximizing the difference between intraclass and interclass SLDM. Then, aEPMs are integrated with multilevel logistic (MLL) model to regularize the raw pixelwise labeling obtained by spectral and spectral–spatial methods, respectively. The graph-cuts-based α β -swap algorithm is modified to optimize the designed energy function. Moreover, to evaluate the final refined results at edges and small details thoroughly, segmentation evaluation metrics are introduced. Experiments conducted on real HSI data denote the superiority of aEPMs in evaluation metrics and region consistency, especially in detail preservation. Numéro de notice : A2021-713 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3035642 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3035642 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98618
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8568 - 8583[article]A viewpoint based approach to the visual exploration of trajectory / Jie Li in Journal of Visual Languages and Computing, vol 41 (August 2017)
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Titre : A viewpoint based approach to the visual exploration of trajectory Type de document : Article/Communication Auteurs : Jie Li, Auteur ; Zhao Xiao, Auteur ; Jun Kong, Auteur Année de publication : 2017 Article en page(s) : pp 41 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] Chine
[Termes IGN] données spatiotemporelles
[Termes IGN] Google Earth
[Termes IGN] jeu de données localisées
[Termes IGN] KML
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] transport collectif
[Termes IGN] transport routier
[Termes IGN] transport urbain
[Termes IGN] visualisation de données
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) We present a new viewpoint-based approach to improving the exploration effects and efficiency of trajectory datasets. Our approach integrates novel trajectory visualization techniques with algorithms for selecting optimal viewpoints to explore the generated visualization. Both the visualization and the viewpoints will be represented in the form of KML, which can be directly rendered in most of off-the-shelf GIS platforms. By playing the viewpoint sequence and directly utilizing the components of GIS platforms to explore the visualization, the overview status, detailed information, and the time variation characteristics of the trajectories can be quickly captured. A case study and a usability experiment have been conducted on an actual public transportation dataset, justifying the effectiveness of our approach. Comparing with the basic exploration approach without viewpoints, we find our approach increases the speed of information retrieval when analyzing trajectory datasets, and enhances user experiences in 3D trajectory exploration. Numéro de notice : A2017-149 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.jvlc.2017.04.001 Date de publication en ligne : 10/04/2017 En ligne : http://doi.org/10.1016/j.jvlc.2017.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84645
in Journal of Visual Languages and Computing > vol 41 (August 2017) . - pp 41 - 53[article]Hierarchical and adaptive phase correlation for precise disparity estimation of UAV images / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
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Titre : Hierarchical and adaptive phase correlation for precise disparity estimation of UAV images Type de document : Article/Communication Auteurs : Jie Li, Auteur ; Yiguang Liu, Auteur ; Shuangli Du, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7092 - 7104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] drone
[Termes IGN] image aérienne
[Termes IGN] modèle numérique de surface
[Termes IGN] stéréoscopieRésumé : (Auteur) When using fixed-window phase correlation (PC) to estimate the disparity of stereo images, the precision is usually rather poor due to large depth differences of scenes and noise, and this problem is specially severe when using unmanned aerial vehicle (UAV) image pairs to extract the digital elevation model of mountain land. To tackle this problem, this paper proposes a hierarchical and adaptive PC, which includes three steps: First, PC with the initialized window is performed to coarsely estimate a disparity value, along with the peak of the Dirichlet function for each pixel; then, an additional round of PC is performed for each pixel using the window of smaller size and with being guided by the coarsely estimated disparity; finally, the previous two steps are iteratively performed until convergence. In particular, using the peak of the Dirichlet function of each pixel in step two, we can drop out the influence of dramatically changing areas such as river; moreover, the scheme can minimize the influence of boundary overreach. The novel scheme has been tested on a large number of UAV images captured at mountainous regions in southwest China, showing that the proposed method is superior to the state-of-the-art methods, especially in handling UAV images of the high mountains and rivers. Numéro de notice : A2016-925 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2595861 En ligne : https://doi.org/10.1109/TGRS.2016.2595861 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83331
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7092 - 7104[article]Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
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Titre : Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation Type de document : Article/Communication Auteurs : Jie Li, Auteur ; Qiangqiang Yuan, Auteur ; Huanfeng Shen, Auteur ; Liangpei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 5425 - 5439 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] bruit (théorie du signal)
[Termes IGN] données clairsemées
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] représentation parcimonieuseRésumé : (Auteur) Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve the quality of images for object detection, classification, and other subsequent applications. It has been reported that noise can be effectively removed using the sparsity in the nonnoise part of the image. With the appreciable redundancy and correlation in HSIs, the denoising performance can be greatly improved if this redundancy and correlation is utilized efficiently in the denoising process. Inspired by this observation, a noise reduction method based on joint spectral-spatial distributed sparse representation is proposed for HSIs, which exploits the intraband structure and the interband correlation in the process of joint sparse representation and joint dictionary learning. In joint spectral-spatial sparse coding, the interband correlation is exploited to capture the similar structure and maintain the spectral continuity. The intraband structure is utilized to adaptively code the spatial structure differences of the different bands. Furthermore, using a joint dictionary learning algorithm, we obtain a dictionary that simultaneously describes the content of the different bands. Experiments on both synthetic and real hyperspectral data show that the proposed method can obtain better results than the other classic methods. Numéro de notice : A2016-902 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2564639 En ligne : https://doi.org/10.1109/TGRS.2016.2564639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83095
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5425 - 5439[article]Efficient multiple-feature learning-based hyperspectral image classification with limited training samples / Chongyue Zhao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
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Titre : Efficient multiple-feature learning-based hyperspectral image classification with limited training samples Type de document : Article/Communication Auteurs : Chongyue Zhao, Auteur ; Xinbo Gao, Auteur ; Ying Wang, Auteur ; Jie Li, Auteur Année de publication : 2016 Article en page(s) : pp 4052 - 4062 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] classification
[Termes IGN] classification bayesienne
[Termes IGN] extraction
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) Linearly derived features have been widely used in hyperspectral image classification to find linear separability of certain classes in recent years. Moreover, nonlinearly transformed features are more effective for class discrimination in real analysis scenarios. However, few efforts have attempted to combine both linear and nonlinear features in the same framework even if they can demonstrate some complementary properties. Moreover, conventional multiple-feature learning-based approaches deal with different features equally, which is not reasonable. This paper proposes an efficient multiple-feature learning-based model with adaptive weights for effectively classifying complex hyperspectral images with limited training samples. A new diversity kernel function is proposed first to simulate the vision perception and analysis procedure of human beings. It could simultaneously evaluate the contrast differences of global features and spatial coherence. Since existing multiple-kernel feature models are always time-consuming, we then design a new adaptive weighted multiple kernel learning method. It employs kernel projection, which could lower the dimensionalities and also learn kernel weights to further discriminate the classification boundaries. For combining both linear and nonlinear features, this paper also proposes a novel decision fusion strategy. The method combines linear and multiple kernel features to balance the classification results of different classifiers. The proposed scheme is tested on several hyperspectral data sets and extended to multisource feature classification environment. The experimental results show that the proposed classification method outperforms most of the existing ones and significantly reduces the computational complexity. Numéro de notice : A2016-878 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2535538 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2535538 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83041
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 4052 - 4062[article]Rethinking big data: A review on the data quality and usage issues / Jianzheng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 115 (May 2016)Permalink