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Unsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters / Ting Mao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8732 - 8744 Titre : Unsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters Type de document : Article/Communication Auteurs : Ting Mao, Auteur ; Wei Huang, Auteur Année de publication : 2019 Article en page(s) : pp 8732 - 8744 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de fusion [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation multirésolution [Termes descripteurs IGN] superpixel Résumé : (auteur) There are many approaches to fuse panchromatic (PAN) and multispectral (MS) images for classification, mainly including sharpening-then-classification methods, classification-then-sharpening methods, and segmentation-then-classification methods. The generalized Chinese restaurant franchise (gCRF) is a segmentation-then-classification-like method to fuse very high resolution (VHR) PAN and MS images for classification, which has the limitation the same as that of the general segmentation-then-classification methods that segmentation errors will affect the subsequent classification. The problems of gCRF are that during the segmentation step, the spatial coherence in the image plane is deficient and the global clusters without spatial position information are used for segmentation, which may lead to undersegmented and disconnected regions in the segmentation results and decrease classification accuracy. In this paper, we propose an improved model, which overcomes the problems of the gCRF during the segmentation step, to increase the classification accuracy by the following two ways: 1) building the spatial coherence in the image plane by introducing neighborhood information of superpixels to construct the subimages and 2) using localized clusters with spatial location information instead of global clusters to measure the similarity between superpixels and segments. The experimental results show that the problems of undersegmentation and disconnected segments are both alleviated, resulting in better classification results in terms of the visual and quantitative aspects. Numéro de notice : A2019-597 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2922672 date de publication en ligne : 17/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2922672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94589 [article]

A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery / F. Cánovas-García in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)  

Public [article]  inGeocarto international > vol 30 n° 7 - 8 (August - September 2015) . - pp 937 - 961 Titre : A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery Type de document : Article/Communication Auteurs : F. Cánovas-García, Auteur ; F. Alonso-Sarría, Auteur Année de publication : 2015 Article en page(s) : pp 937 - 961 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] facteur d'échelle [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] mise à l'échelle [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation multirésolution Résumé : (Auteur) The results obtained using the object-based image analysis approach for remote sensing image analysis depend strongly on the quality of the segmentation step. In this paper, to optimize the scale parameter in a multiresolution segmentation, we analyse a high-resolution image of a large and heterogeneous agricultural area. This approach is based on using a set of agricultural plots extracted from official maps as uniform spatial units. The scale parameter is then optimized in each uniform spatial unit. Intra-object and inter-object heterogeneity measurements are used to evaluate each segmentation. To avoid subsegmentation, some oversegmentation is allowed, but is attenuated in a second step using the spectral difference segmentation algorithm. The statistical distribution of the scale parameter is not equal in all land uses, indicating the soundness of this local approach. A quantitative assessment of the results was also conducted for the different land covers. The results indicate that the spectral contrast between objects is larger with the local approach than with the global approach. These differences were statistically significant in all land uses except irrigated fruit trees and greenhouses. In the absence of subsegmentation, this suggests that the objects will be placed far apart in the space of variables, even if they are very close in the physical space. This is an obvious advantage in a subsequent classification of the objects. Numéro de notice : A2015-505 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1004131 date de publication en ligne : 18/02/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1004131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77422 [article]

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Toward evaluating multiscale segmentations of high spatial resolution remote sensing images / Xueliang Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3694 - 3706 Titre : Toward evaluating multiscale segmentations of high spatial resolution remote sensing images Type de document : Article/Communication Auteurs : Xueliang Zhang, Auteur ; Pengfeng Xiao, Auteur ; Xuezhi Feng, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 3694 - 3706 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image numérique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] Chine [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] segmentation multirésolution Résumé : (Auteur) Object-based analysis of high spatial resolution remote sensing images addresses the matter of multiscale segmentation. However, existing segmentation evaluation methods mainly focus on single-scale segmentation. In this paper, we examine the issue of supervised multiscale segmentation evaluation and propose two discrepancy measures to determine the manner in which geographic objects are delineated by multiscale segmentations. A QuickBird scene in Hangzhou, China, is used to conduct the evaluation. The results reveal the effectiveness of the proposed measures, in terms of method comparison and parameter optimization, for multiscale segmentation of high spatial resolution images. Moreover, meaningful indications for selecting suitable multiple segmentation scales are presented. The proposed measures are applicable to performance evaluation and parameter optimization for multiscale segmentation algorithms. Numéro de notice : A2015-320 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76573 [article]

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Fast hierarchical segmentation of high-resolution remote sensing images with adaptative edge penalty / Xuellang Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 1 (January 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 1 (January 2014) . - pp 71 - 80 Titre : Fast hierarchical segmentation of high-resolution remote sensing images with adaptative edge penalty Type de document : Article/Communication Auteurs : Xuellang Zhang, Auteur ; Pending Xiao, Auteur ; Xuezhi Feng, Auteur Année de publication : 2014 Article en page(s) : pp 71 - 80 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] segmentation multirésolution Résumé : (Auteur) A fast hierarchical segmentation method (FHS) for high-resolution remote sensing (HR) image is proposed in the paper. FHS is completely unsupervised. It is characterized by two aspects. First, the hierarchical segmentation process is accelerated by the improved linear nearest neighbor graph (LNNG) model and the segment tree model. It runs faster than other existing hierarchical segmentation methods, and can produce multi-resolution segmentations in time linear to the image size. Second, an adaptive edge penalty function is introduced to formulate the merging criterion, serving as a semantic factor. A set of QuickBird, WorldView, and aerial images is used to test the proposed method. The experiments show that the multi-resolution segmentations produced by FHS can represent objects at different scales very well. Moreover, the adaptive edge penalty function helps to remove meaningless weak edges within objects, enclosing the relation between segments and real-world objects. Numéro de notice : A2014-093 Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.1.71 En ligne : https://doi.org/10.14358/PERS.80.1.71 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32998 [article]

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Range of categorical associations for comparison of maps with mixed pixels / Robert Gilmore Pontius in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 8 (August 2009)

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 8 (August 2009) . - pp 963 - 969 Titre : Range of categorical associations for comparison of maps with mixed pixels Type de document : Article/Communication Auteurs : Robert Gilmore Pontius, Auteur ; J. Connors, Auteur Année de publication : 2009 Article en page(s) : pp 963 - 969 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] décomposition du pixel [Termes descripteurs IGN] image multi sources [Termes descripteurs IGN] Massachusetts (Etats-Unis) [Termes descripteurs IGN] matrice [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation multirésolution Résumé : (Auteur) This paper presents a method to compare maps that contain pixels that have partial membership to multiple categories, i.e., mixed or soft classified pixels. The method quantifies ranges for associations among categories based upon possible variations in sub-pixel spatial allocation. The paper derives the mathematical equations for constructing the range of associations based on three types of cross-tabulation matrices, the greatest matrix, the random matrix, and the least matrix. We demonstrate how the analysis can be combined with multiple resolution map comparison to specify the resolution at which clusters exist on a single map or between two maps. The method produces a range that reflects the amount of uncertainty in the categorical associations. We illustrate the procedure with both a simple example and data from the Plum Island Ecosystems study site in Massachusetts, USA. Numéro de notice : A2009-309 Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29939 [article]

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Etude géomorphologique des coulées de lave du piton de la fournaise / Astrid Gladys (2008)

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Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition / Jing Tian in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)

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Analyse orientée-objet pour la détection de bâtiments à partir d'une image satellitaire à très haute résolution spatiale / Hakim Boulaassal in XYZ, n° 101 (décembre 2004 - février 2005)

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