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Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data : A case study of Wuhan, Central China / Xin Huang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 119 - 131 Titre : Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data : A case study of Wuhan, Central China Type de document : Article/Communication Auteurs : Xin Huang, Auteur ; Ying Wang, Auteur Année de publication : 2019 Article en page(s) : pp 119 - 131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] arbre urbain [Termes IGN] Chine [Termes IGN] ilot thermique urbain [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-TIRS [Termes IGN] image ZiYuan-3 [Termes IGN] morphologie urbaine [Termes IGN] régression multiple [Termes IGN] température au sol [Termes IGN] Wuhan (Chine) Résumé : (Auteur) The Urban heat island (UHI) effect is an increasingly serious problem in urban areas. Information on the driving forces of intra-urban temperature variation is crucial for ameliorating the urban thermal environment. Although prior studies have suggested that urban morphology (e.g., landscape pattern, land-use type) can significantly affect land surface temperature (LST), few studies have explored the comprehensive effect of 2D and 3D urban morphology on LST in different urban functional zones (UFZs), especially at a fine scale. Therefore, in this research, we investigated the relationship between 2D/3D urban morphology and summer daytime LST in Wuhan, a representative megacity in Central China, which is known for its extremely hot weather in summer, by adopting high-resolution remote sensing data and geographical information data. The “urban morphology” in this study consists of 2D urban morphological parameters, 3D urban morphological parameters, and UFZs. Our results show that: (1) The LST is significantly related to 2D and 3D urban morphological parameters, and the scattered distribution of buildings with high rise can facilitate the mitigation of LST. Although sky view factor (SVF) is an important measure of 3D urban geometry, its influence on LST is complicated and context-dependent. (2) Trees are the most influential factor in reducing LST, and the cooling efficiency mainly depends on their proportions. The fragmented and irregular distribution of grass/shrubs also plays a significant role in alleviating LST. (3) With respect to UFZs, the residential zone is the largest heat source, whereas the highest LST appears in commercial and industrial zones. (4) Results of the multivariate regression and variation partitioning indicate that the relative importance of 2D and 3D urban morphological parameters on LST varies among different UFZs and 2D morphology outperforms 3D morphology in LST modulation. The results are generally consistent in spring, summer and autumn. These findings can provide insights for urban planners and designers on how to mitigate the surface UHI (SUHI) effect via rational landscape design and urban management during summer daytime. Numéro de notice : A2019-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.010 Date de publication en ligne : 22/04/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92869 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019063	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019062	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 4052 - 4062 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 noyau Résumé : (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 [article]

A multi-scale plane-detection method based on the Hough transform and region growing / Xiaoxu Leng in Photogrammetric record, vol 31 n° 154 (June - August 2016)  

Public [article]  inPhotogrammetric record > vol 31 n° 154 (June - August 2016) . - pp 166 - 192 Titre : A multi-scale plane-detection method based on the Hough transform and region growing Type de document : Article/Communication Auteurs : Xiaoxu Leng, Auteur ; Jun Xiao, Auteur ; Ying Wang, Auteur Année de publication : 2016 Article en page(s) : pp 166 - 192 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] détection automatique [Termes IGN] modélisation 3D [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] traitement d'image [Termes IGN] transformation de Hough Résumé : (auteur) The detection of planes from 3D point clouds plays an important role in fields such as 3D modelling, rock mechanics, registration and robotics. A multi-scale plane-detection method is proposed, based on extracting growth units using the Hough transform and subsequent region growing into actual plane boundaries. Because the approximate geometric features of the original plane area can be obtained from the growth units, discriminant conditions for adaptive plane growing could be provided for the growing stage. In order to detect physical planes at different scales, a strategy of multi-scale normal estimation is introduced. Simulated icosahedron point clouds, together with actual engineering point clouds, were used to test the performance of the proposed method. Experimental results show that planar point clouds could be detected effectively, especially with rock-mass engineering applications. Numéro de notice : A2016-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12145 Date de publication en ligne : 17/06/2016 En ligne : https://doi.org/10.1111/phor.12145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81473 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
106-2016021	RAB	Revue	Centre de documentation	En réserve L003	Disponible