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Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping / Luca Demarchi in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
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Titre : Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping Type de document : Article/Communication Auteurs : Luca Demarchi, Auteur ; Frank Canters, Auteur ; Claude Cariou, Auteur ; Giorgio Licciardi, Auteur ; Jonathan Cheung-Wai Chan, Auteur Année de publication : 2014 Article en page(s) : pp 166 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Airborne Prism Experiment
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image APEX
[Termes IGN] image hyperspectrale
[Termes IGN] Perceptron multicoucheRésumé : (Auteur) Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas. Numéro de notice : A2014-018 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32923
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 166 - 179[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Assessment of the image misregistration effects on object-based change detection / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
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Titre : Assessment of the image misregistration effects on object-based change detection Type de document : Article/Communication Auteurs : Gang Chen, Auteur ; Kaiguang Zhao, Auteur ; Ryan Powers, Auteur Année de publication : 2014 Article en page(s) : pp 19 - 27 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] classification orientée objet
[Termes IGN] détection de changement
[Termes IGN] estimation de précision
[Termes IGN] image multitemporelle
[Termes IGN] image SPOT 5Résumé : (Auteur) High-spatial resolution remote sensing imagery provides unique opportunities for detailed characterization and monitoring of landscape dynamics. To better handle such data sets, change detection using the object-based paradigm, i.e., object-based change detection (OBCD), have demonstrated improved performances over the classic pixel-based paradigm. However, image registration remains a critical pre-process, with new challenges arising, because objects in OBCD are of various sizes and shapes. In this study, we quantified the effects of misregistration on OBCD using high-spatial resolution SPOT 5 imagery (5 m) for three types of landscapes dominated by urban, suburban and rural features, representing diverse geographic objects. The experiments were conducted in four steps: (i) Images were purposely shifted to simulate the misregistration effect. (ii) Image differencing change detection was employed to generate difference images with all the image-objects projected to a feature space consisting of both spectral and texture variables. (iii) The changes were extracted using the Mahalanobis distance and a change ratio. (iv) The results were compared to the ‘real’ changes from the image pairs that contained no purposely introduced registration error. A pixel-based change detection method using similar steps was also developed for comparisons. Results indicate that misregistration had a relatively low impact on object size and shape for most areas. When the landscape is comprised of small mean object sizes (e.g., in urban and suburban areas), the mean size of ‘change’ objects was smaller than the mean of all objects and their size discrepancy became larger with the decrease in object size. Compared to the results using the pixel-based paradigm, OBCD was less sensitive to the misregistration effect, and the sensitivity further decreased with an increase in local mean object size. However, high-spatial resolution images typically have higher spectral variability within neighboring pixels than the relatively low resolution datasets. As a result, accurate image registration remains crucial to change detection even if an object-based approach is used. Numéro de notice : A2014-008 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32913
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 19 - 27[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Collaborative sparse regression for hyperspectral unmixing / Marian-Daniel Iordache in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)
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Titre : Collaborative sparse regression for hyperspectral unmixing Type de document : Article/Communication Auteurs : Marian-Daniel Iordache, Auteur ; José Bioucas-Dias, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2014 Article en page(s) : pp 341 - 354 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] accentuation d'image
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] régressionRésumé : (Auteur) Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In this paper, we present a refinement of the sparse unmixing methodology recently introduced which exploits the usual very low number of endmembers present in real images, out of a very large library. Specifically, we adopt the collaborative (also called “multitask” or “simultaneous”) sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent approach. Numéro de notice : A2014-038 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2240001 En ligne : https://doi.org/10.1109/TGRS.2013.2240001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32943
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 1 (January 2014) . - pp 341 - 354[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014011A RAB Revue Centre de documentation En réserve L003 Disponible Combining top-down and bottom-up approaches for building detection in a single very high resolution satellite image / Mahmoud Mohammed Sidi Youssef (2014)
Titre : Combining top-down and bottom-up approaches for building detection in a single very high resolution satellite image Type de document : Article/Communication Auteurs : Mahmoud Mohammed Sidi Youssef, Auteur ; Clément Mallet , Auteur ; Nesrine Chehata , Auteur ; Arnaud Le Bris , Auteur ; Adrien Gressin , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2014 Conférence : IGARSS 2014, International Geoscience And Remote Sensing Symposium 13/07/2014 18/07/2014 Québec Québec - Canada Proceedings IEEE Importance : pp 4820 - 4823 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] détection du bâti
[Termes IGN] image multibande
[Termes IGN] image Pléiades-HR
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] reconnaissance de formesRésumé : (auteur) Building detection from geospatial optical images has been a popular topic of research for the last twenty years and in particular with the emergence of very high resolution satellites. Existing methods exhibit various flaws and prevent them from being efficient at large scales of space and time: they are context-dependent, require a tedious parameter tuning or several data sources. In this paper, we propose a fully automatic method that alleviates some of these issues by combining the strengths of bottom-up and top-down approaches, i.e., of both classification and pattern recognition algorithms. This allows to correctly detect the objects by geometric prior knowledge while finely delineating their borders and preserving their shapes. The method is evaluated over a complex area of more than 230 buildings using a 0.5 m multispectral pansharpened Pleiades image. Numéro de notice : C2014-028 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2014.6947573 Date de publication en ligne : 10/11/2014 En ligne : http://dx.doi.org/10.1109/IGARSS.2014.6947573 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83398 Comparaison de méthodes d'extraction automatique à partir d'images multispectrales / Valerio Baiocchi in Géomatique expert, n° 96 (01/01/2014)
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Titre : Comparaison de méthodes d'extraction automatique à partir d'images multispectrales Type de document : Article/Communication Auteurs : Valerio Baiocchi, Auteur ; Maria Vittoria Milone, Auteur ; Martina Mormile, Auteur ; R. Brigante, Auteur ; Donatella Dominici., Auteur Année de publication : 2014 Article en page(s) : pp 8 - 15 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Abruzzes
[Termes IGN] analyse comparative
[Termes IGN] classification dirigée
[Termes IGN] classification orientée objet
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] dommage matériel
[Termes IGN] extraction automatique
[Termes IGN] image multibande
[Termes IGN] séismeRésumé : (Auteur) [Introduction] [...] Cet article vise à comparer deux algorithmes de classification : l'approche pixel et l'approche objet, dans le cadre d'études portant sur l'évaluation des dommages créés par une catastrophe naturelle, par exemple un séisme. Nous allons donc utiliser comme zone d'étude la ville italienne de l'Aquila, récemment frappée par un tremblement de terre aux conséquences majeures. Numéro de notice : A2014-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32947
in Géomatique expert > n° 96 (01/01/2014) . - pp 8 - 15[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 265-2014011 RAB Revue Centre de documentation En réserve L003 Disponible IFN-001-P001541 PER Revue Nogent-sur-Vernisson Salle périodiques Disponible PermalinkDétection de bâtiments à partir d’une image satellitaire par combinaison d’approches ascendante et descendante / Mohamed Mahmoud Sidi Yousseff (2014)PermalinkFast 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)PermalinkHierarchical extraction of landslides from multiresolution remotely sensed optical images / Camille Kurtz in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkHyperspectral image classification using nearest feature line embedding approach / Yang-Lang Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)PermalinkImagerie terrestre urbaine : vers une méthode physique d'estimation de la réflectance / Fabien Coubard (2014)PermalinkA local contrast method for small infrared target detection / C.L. 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Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)PermalinkRestoration of information obscured by mountainous shadows through Landsat TM/ETM+ images without the use of DEM data : A new method / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)PermalinkUse intermediate results of wrapper band selection methods: A first step toward the optimization of spectral configuration for land cover classifications / Arnaud Le Bris (2014)PermalinkAn entropy-based multispectral image classification algorithm / Di Long in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)PermalinkAutomated detection of buildings from single VHR multispectral images using shadow information and graph cuts / Ali Ozgun Ok in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)PermalinkGaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval / Jochem Verrlest in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)PermalinkHierarchical method of urban building extraction inspired by human perception / Chao Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)PermalinkIllustrating the temporal progress of environmental change / Joann W. 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Everitt in Geocarto international, vol 28 n° 5-6 (August - October 2013)PermalinkBuilding a forward-mode three-dimensional reflectance model for topographic normalization of High-Resolution (1–5 m) imagery: validation phase in a forested environment / Stéphane Couturier in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkComparaison entre les méthodes J-SEG et MeanShift : application sur des données THRS / Rabia Sarah Cheriguene in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)PermalinkDeblurring and sparse unmixing for hyperspectral images / Xi-Le Zhao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkDevelopment of a 3-D urbanization index using digital terrain models for surface urban heat island effects / Chih-Da Wu in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)PermalinkEffects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm / Jaehoon Jung in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)PermalinkGraph-regularized low-rank representation for destriping of hyperspectral images / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkIndependent two-step thresholding of binary images in inter-annual land cover change/no-change identification / Priyakant Sinha in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)PermalinkMissing-area reconstruction in multispectral images under a compressive sensing perspective / Luca Lorenzi in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)Permalink