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Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects / Jike Chen in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 89 (July 2020) . - n° 102060 Titre : Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects Type de document : Article/Communication Auteurs : Jike Chen, Auteur ; Shuanggen Jin, Auteur ; Peijun Du, Auteur Année de publication : 2020 Article en page(s) : n° 102060 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arbre urbain [Termes IGN] canopée [Termes IGN] carte de la végétation [Termes IGN] couvert forestier [Termes IGN] données lidar [Termes IGN] ilot thermique urbain [Termes IGN] modèle numérique de terrain [Termes IGN] Nankin (Kiangsou) [Termes IGN] occupation du sol [Termes IGN] régression linéaire [Termes IGN] semis de points [Termes IGN] température au sol Résumé : (auteur) The urban heat island (UHI) is increasingly recognized as a serious, worldwide problem because of urbanization and climate change. Urban vegetation is capable of alleviating UHI and improving urban environment by shading together with evapotranspiration. While the impacts of abundance and spatial configuration of vegetation on land surface temperature (LST) have been widely examined, very little attention has been paid to the role of vertical structure of vegetation in regulating LST. In this study, we investigated the relationships between horizontal/vertical structure characteristics of urban tree canopy and LST as well as diurnal divergence in Nanjing City, China, with the help of high resolution vegetation map, Light Detection and Ranging (LiDAR) data and various statistical analysis methods. The results indicated that composition, configuration and vertical structure of tree canopy were all significantly related to both daytime LST and nighttime LST. Tree canopy showed stronger influence on LST during the day than at night. Note that the contribution of composition of tree canopy to explaining spatial heterogeneity of LST, regardless of day and night, was the highest, followed by vertical structure and configuration. Combining composition, configuration and vertical structure of tree canopy can take advantage of their respective advantages, and best explain variation in both daytime LST and nighttime LST. As for the independent importance of factors affecting spatial variation of LST, percent cover of tree canopy (PLAND), mean tree canopy height (TH_Mean), amplitude of tree canopy height (TA) and patch cohesion index (COHESION) were the most influential during the day, while the most important variables were PLAND, maximum height of tree canopy (TH_Max), variance of tree canopy height (TH_SD) and COHESION at night. This research extends our understanding of the impacts of urban trees on the UHI effect from the horizontal to three-dimensional space. In addition, it may offer sustainable and effective strategies for urban designers and planners to cope with increasing temperature. Numéro de notice : A2020-715 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2020.102060 Date de publication en ligne : 25/02/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102060 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96285 [article]

Change detection based on stacked generalization system with segmentation constraint / Kun Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 11 (November 2018) . - pp 733 - 741 Titre : Change detection based on stacked generalization system with segmentation constraint Type de document : Article/Communication Auteurs : Kun Tan, Auteur ; Yusha Zhang, Auteur ; Qian Du, Auteur ; Peijun Du, Auteur ; Xiao Jin, Auteur ; Jiayi Li, Auteur Année de publication : 2018 Article en page(s) : pp 733 - 741 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] détection de changement [Termes IGN] image Quickbird [Termes IGN] image ZiYuan-3 [Termes IGN] segmentation d'image Résumé : (Auteur) Change detection based on a multi-classifier ensemble system can take advantage of multiple classifiers to extract change information in remote sensing images. In this paper, an efficient heterogeneous ensemble algorithm, i.e., the stacked generalization (SG) combined with image segmentation, is proposed to construct a simple multi-classifier ensemble system that can offer better detection accuracy with lower computational cost. Due to the rich spatial information in high-spatial-resolution remote sensing images, structure texture (morphological) and statistical texture features are extracted to construct the input data to the ensemble system along with spectral features. In addition, constrained analysis on segmented objects integrates the smaller heterogeneity segmentation map and pixel-wise change map to generate the final change map. The experiments were carried out on two ZY-3 and a QuickBird dataset. The results show that the proposed algorithm can integrate the advantages of both pixel-wise ensemble and object-oriented methods, and effectively improve the accuracy and stability of change detection. Numéro de notice : A2018-485 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.11.733 Date de publication en ligne : 01/11/2018 En ligne : https://doi.org/10.14358/PERS.84.11.733 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91210 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2018111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images / Sicong Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2733 - 2748 Titre : Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images Type de document : Article/Communication Auteurs : Sicong Liu, Auteur ; Lorenzo Bruzzone, Auteur ; Francesca Bovolo, Auteur ; Peijun Du, Auteur Année de publication : 2016 Article en page(s) : pp 2733 - 2748 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse infrapixellaire [Termes IGN] détection de changement [Termes IGN] image hyperspectrale [Termes IGN] image multitemporelle Résumé : (Auteur) This paper presents a novel multitemporal spectral unmixing (MSU) approach to address the challenging multiple-change detection problem in bitemporal hyperspectral (HS) images. Differently from the state-of-the-art methods that are mainly designed at a pixel level, the proposed technique investigates the spectral-temporal variations at a subpixel level. The considered change detection (CD) problem is analyzed in a multitemporal domain, where a bitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted according to an automatic and unsupervised technique. Then, a change analysis strategy is designed to distinguish the change and no-change MT-EMs. An endmember-grouping scheme is applied to the changed MT-EMs to detect the unique change classes. Finally, the considered multiple-change detection problem is solved by analyzing the abundances of the change and no-change classes and their contribution to each pixel. The proposed approach has been validated on both simulated and real multitemporal HS data sets presenting multiple changes. Experimental results confirmed the effectiveness of the proposed method. Numéro de notice : A2016-846 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2505183 En ligne : https://doi.org/10.1109/TGRS.2015.2505183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82927 [article]

Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features / Peijun Du in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 38 - 53 Titre : Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features Type de document : Article/Communication Auteurs : Peijun Du, Auteur ; Alim Samat, Auteur ; Björn Waske, Auteur ; Sicong Liu, Auteur ; Zhenhong Li, Auteur Année de publication : 2015 Article en page(s) : pp 38 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données polarimétriques [Termes IGN] image Radarsat [Termes IGN] polarimétrie radar [Termes IGN] Rotation Forest classification [Termes IGN] texture d'image Résumé : (auteur) Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-weather, day and night observation and high resolution capabilities. The collected data are usually sorted in Sinclair matrix, coherence or covariance matrices which are directly related to physical properties of natural media and backscattering mechanism. Additional information related to the nature of scattering medium can be exploited through polarimetric decomposition theorems. Accordingly, PolSAR image classification gains increasing attentions from remote sensing communities in recent years. However, the above polarimetric measurements or parameters cannot provide sufficient information for accurate PolSAR image classification in some scenarios, e.g. in complex urban areas where different scattering mediums may exhibit similar PolSAR response due to couples of unavoidable reasons. Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary information between polarimetric and spatial features may also contribute to PolSAR image classification. Therefore, the roles of textural features such as contrast, dissimilarity, homogeneity and local range, morphological profiles (MPs) in PolSAR image classification are investigated using two advanced ensemble learning (EL) classifiers: Random Forest and Rotation Forest. Supervised Wishart classifier and support vector machines (SVMs) are used as benchmark classifiers for the evaluation and comparison purposes. Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies. Rotation Forest can get better accuracy than SVM and Random Forest, in the meantime, Random Forest is much faster than Rotation Forest. Numéro de notice : A2015-706 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.03.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.03.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78342 [article]

Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2532 - 2546 Titre : Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields Type de document : Article/Communication Auteurs : Junshi Xia, Auteur ; Jocelyn Chanussot, Auteur ; Peijun Du, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2532 - 2546 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse en composantes principales [Termes IGN] champ aléatoire de Markov [Termes IGN] classification et arbre de régression [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] performance [Termes IGN] Rotation Forest classification Résumé : (Auteur) In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification. Numéro de notice : A2015-519 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2361618 En ligne : https://doi.org/10.1109/TGRS.2014.2361618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77526 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Hierarchical unsupervised change detection in multitemporal hyperspectral images / S. Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

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Spectral–spatial classification of hyperspectral data via morphological component analysis-based image separation / Zhaohui Xue in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

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