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Attribute profiles on derived features for urban land cover classification / Bharath Bhushan Damodaran in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 183 - 193 Titre : Attribute profiles on derived features for urban land cover classification Type de document : Article/Communication Auteurs : Bharath Bhushan Damodaran, Auteur ; Joachim Höhle, Auteur ; Sébastien Lefèvre, Auteur Année de publication : 2017 Article en page(s) : pp 183 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attribut [Termes IGN] classification dirigée [Termes IGN] données auxiliaires [Termes IGN] hauteur ellipsoïdale [Termes IGN] image aérienne [Termes IGN] image multibande [Termes IGN] indice de végétation [Termes IGN] occupation du sol [Termes IGN] précision de la classification [Termes IGN] zone urbaine Résumé : (Auteur) This research deals with the automatic generation of 2D land cover maps of urban areas using very high resolution multispectral aerial imagery. The appropriate selection of classifier and attributes is important to achieve high thematic accuracies. In this paper, new attributes are generated to increase the discriminative power of auxiliary information provided by remote sensing images. The generated attributes are derived from the vegetation index and elevation information using morphological attribute profiles. The extended experimental evaluation and comparison of attribute profile-based mapping solutions is conducted to derive the optimal combinations of attributes required for classification and to understand the genericity of attributes on a range of classifiers, i.e., various combinations of attributes and classifiers. Experimental results with two high resolution images show that the proposed attributes derived on auxiliary information outperform the existing attribute profiles computed on original image and its principal components. Numéro de notice : A2017-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.183 En ligne : https://doi.org/10.14358/PERS.83.3.183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84422 [article]

Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye 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 1544 - 1562 Titre : Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification Type de document : Article/Communication Auteurs : Minchao Ye, Auteur ; Yuntao Qian, Auteur ; Jun Zhou, Auteur ; Yuan Yan Tang, Auteur Année de publication : 2017 Article en page(s) : pp 1544 - 1562 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage dirigé [Termes IGN] classification dirigée [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] occupation du sol [Termes IGN] régression logistique Résumé : (Auteur) A big challenge of hyperspectral image (HSI) classification is the small size of labeled pixels for training classifier. In real remote sensing applications, we always face the situation that an HSI scene is not labeled at all, or is with very limited number of labeled pixels, but we have sufficient labeled pixels in another HSI scene with the similar land cover classes. In this paper, we try to classify an HSI scene containing no labeled sample or only a few labeled samples with the help of a similar HSI scene having a relative large size of labeled samples. The former scene is defined as the target scene, while the latter one is the source scene. We name this classification problem as cross-scene classification. The main challenge of cross-scene classification is spectral shift, i.e., even for the same class in different scenes, their spectral distributions maybe have significant deviation. As all or most training samples are drawn from the source scene, while the prediction is performed in the target scene, the difference in spectral distribution would greatly deteriorate the classification performance. To solve this problem, we propose a dictionary learning-based feature-level domain adaptation technique, which aligns the spectral distributions between source and target scenes by projecting their spectral features into a shared low-dimensional embedding space by multitask dictionary learning. The basis atoms in the learned dictionary represent the common spectral components, which span a cross-scene feature space to minimize the effect of spectral shift. After the HSIs of two scenes are transformed into the shared space, any traditional HSI classification approach can be used. In this paper, sparse logistic regression (SRL) is selected as the classifier. Especially, if there are a few labeled pixels in the target domain, multitask SRL is used to further promote the classification performance. The experimental results on synthetic and real HSIs show the advantages of the proposed method for cross-scene classification. Numéro de notice : A2017-157 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2627042 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2627042 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84694 [article]

Unsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 225 - 236 Titre : Unsupervised object-based differencing for land-cover change detection Type de document : Article/Communication Auteurs : Jinxia Zhu, Auteur ; Yanjun Su, Auteur ; Qinghua Guo, Auteur ; Thomas C. Harmon, Auteur Année de publication : 2017 Article en page(s) : pp 225 - 236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme espérance-maximisation [Termes IGN] altération [Termes IGN] autocorrélation [Termes IGN] changement d'occupation du sol [Termes IGN] Chine [Termes IGN] classification non dirigée [Termes IGN] classification orientée objet [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] image SPOT-HRV [Termes IGN] occupation du sol [Termes IGN] traitement d'image Résumé : (Auteur) One main problem of the spectral decomposition-based change detection method is the lack of efficient automatic techniques for developing the difference image. Traditional techniques generally assume that gray-level values in a difference image are independent and multitemporal images are co-registered/rectified perfectly without error. However, such assumptions are often violated because of the inevitable image misregistration and the interference of correlations between spectral bands. This study proposes an automated method based on the object-based multivariate alteration detection/maximum autocorrelation factor approach and the Gaussian mixture model-expectation maximization algorithm to obtain unsupervised difference images. This procedure is applied to bi-temporal (2005 and 2006) SPOT-HRV images at Panyu District Ponds, China. Results show that the proposed method successfully excludes the correlations of spectral bands and the influence of misregistration, as evidenced by a higher accuracy (up to 93.6 percent). These unique technical characteristics make this analytical framework suitable for detecting changes. Numéro de notice : A2017-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.225 En ligne : https://doi.org/10.14358/PERS.83.3.225 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84424 [article]

Characterizing the relationship between land use land cover change and land surface temperature / Duy X. Tran in ISPRS Journal of photogrammetry and remote sensing, vol 124 (February 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 124 (February 2017) . - pp 119 - 132 Titre : Characterizing the relationship between land use land cover change and land surface temperature Type de document : Article/Communication Auteurs : Duy X. Tran, Auteur ; Filiberto Pla, Auteur ; Pedro Latorre-Carmona, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 119 - 132 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement d'occupation du sol [Termes IGN] changement d'utilisation du sol [Termes IGN] Hanoï [Termes IGN] ilot thermique urbain [Termes IGN] modèle de simulation [Termes IGN] occupation du sol [Termes IGN] outil d'aide à la décision [Termes IGN] température au sol [Termes IGN] urbanisation [Termes IGN] utilisation du sol Résumé : (Auteur) Exploring changes in land use land cover (LULC) to understand the urban heat island (UHI) effect is valuable for both communities and local governments in cities in developing countries, where urbanization and industrialization often take place rapidly but where coherent planning and control policies have not been applied. This work aims at determining and analyzing the relationship between LULC change and land surface temperature (LST) patterns in the context of urbanization. We first explore the relationship between LST and vegetation, man-made features, and cropland using normalized vegetation, and built-up indices within each LULC type. Afterwards, we assess the impacts of LULC change and urbanization in UHI using hot spot analysis (Getis-Ord Gi∗ statistics) and urban landscape analysis. Finally, we propose a model applying non-parametric regression to estimate future urban climate patterns using predicted land cover and land use change. Results from this work provide an effective methodology for UHI characterization, showing that (a) LST depends on a nonlinear way of LULC types; (b) hotspot analysis using Getis Ord Gi∗ statistics allows to analyze the LST pattern change through time; (c) UHI is influenced by both urban landscape and urban development type; (d) LST pattern forecast and UHI effect examination can be done by the proposed model using nonlinear regression and simulated LULC change scenarios. We chose an inner city area of Hanoi as a case-study, a small and flat plain area where LULC change is significant due to urbanization and industrialization. The methodology presented in this paper can be broadly applied in other cities which exhibit a similar dynamic growth. Our findings can represent an useful tool for policy makers and the community awareness by providing a scientific basis for sustainable urban planning and management. Numéro de notice : A2017-072 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.01.001 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.01.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84308 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017023	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2017022	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping / Mirco Sturari in European journal of remote sensing, vol 50 n° 1 (2017)  

Public [article]  inEuropean journal of remote sensing > vol 50 n° 1 (2017) . - pp 1 - 17 Titre : Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping Type de document : Article/Communication Auteurs : Mirco Sturari, Auteur ; Emanuele Frontoni, Auteur ; Roberto Pierdicca, Auteur ; Adriano Mancini, Auteur ; Eva Savina Malinverni, Auteur ; Anna Nora Tassetti, Auteur ; Primo Zingaretti, Auteur Année de publication : 2017 Article en page(s) : pp 1 - 17 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] base de données d'occupation du sol [Termes IGN] carte d'occupation du sol [Termes IGN] classification hybride [Termes IGN] données altimétriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image multibande [Termes IGN] intégration de données [Termes IGN] occupation du sol Résumé : (Auteur) The combination of elevation data together with multispectral high-resolution images is a new methodology for obtaining land use/land cover classification. It represents a step forward for both the accuracy and automation of LULC applications and allows users to setup thematic assignments through rules based on feature attributes and human expert interpretation of land usage. The synergy between different types of information means that LiDAR can give new hints at both the segmentation and hybrid classification steps, leading to a joint use of multispectral, spatial and elevation data. The output is a thematic map characterized by a custom-designed legend that is able to discriminate between land cover classes with similar spectral characteristics (level 3 of the CLC legend). Experimental results from a hilly farmland area with some urban structures (Musone river basin, Ancona, Italy) are used to highlight how the proposed methodology enhances land cover classification in heterogeneous environments. Numéro de notice : A2017-043 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2017.1274572 En ligne : http://doi.org/10.1080/22797254.2017.1274572 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84213 [article]

Analyse de séries temporelles d’images Sentinel et intégration de connaissances pour la classification en milieu agricole / Simon Bailly (2017) 

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Caractérisation de la végétation de Rennes Métropole par relevé LiDAR en vue de sa modélisation / Clément Doceul (2017) 

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Simulation et évaluation de l’influence de documents d’aménagements sur la qualité des milieux écologiques / Jennifer Benchetrit (2017) 

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Sols artificialisés et processus d’artificialisation des sols : déterminants, impacts et leviers d’action / Béatrice Béchet (2017)    

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Synthèse [de la] journée d’information et d’échanges « Copernicus » / Anonyme (2017) 

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Télédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)

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Exposure-related forest-steppe: A diverse landscape type determined by topography and climate / Martin Hais in Journal of Arid Environments, vol 135 (December 2016)  

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A global study of NDVI difference among moderate-resolution satellite sensors / Xingwang Fan in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)  

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n° 2 - octobre 2016 - Atlas régional de l'occupation des sols en France (Bulletin de Datalab) / Service de l'observation et des statistiques  

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Distributed texture-based land cover classification algorithm using hidden Markov model for multispectral data / S. Jenicka in Survey review, vol 48 n° 351 (October 2016)  

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