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Semi-automated analysis of high-resolution aerial images to quantify docks in glacial lakes / Marcus W. Beck in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)
[article]
Titre : Semi-automated analysis of high-resolution aerial images to quantify docks in glacial lakes Type de document : Article/Communication Auteurs : Marcus W. Beck, Auteur ; Bruce Vondracek, Auteur ; Lorin Hatch, Auteur ; Jason Vinje, Auteur Année de publication : 2013 Article en page(s) : pp 60 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'objet
[Termes IGN] image aérienne
[Termes IGN] lac
[Termes IGN] Minnesota (Etats-Unis)
[Termes IGN] pêche
[Termes IGN] ressources aquatiques
[Termes IGN] surveillance écologiqueRésumé : (Auteur) Lake resources can be negatively affected by environmental stressors originating from multiple sources and different spatial scales. Shoreline development, in particular, can negatively affect lake resources through decline in habitat quality, physical disturbance, and impacts on fisheries. The development of remote sensing techniques that efficiently characterize shoreline development in a regional context could greatly improve management approaches for protecting and restoring lake resources. The goal of this study was to develop an approach using high-resolution aerial photographs to quantify and assess docks as indicators of shoreline development. First, we describe a dock analysis workflow that can be used to quantify the spatial extent of docks using aerial images. Our approach incorporates pixel-based classifiers with object-based techniques to effectively analyze high-resolution digital imagery. Second, we apply the analysis workflow to quantify docks for 4261 lakes managed by the Minnesota Department of Natural Resources. Overall accuracy of the analysis results was 98.4% (87.7% based on View the MathML source) after manual post-processing. The analysis workflow was also 74% more efficient than the time required for manual digitization of docks. These analyses have immediate relevance for resource planning in Minnesota, whereas the dock analysis workflow could be used to quantify shoreline development in other regions with comparable imagery. These data can also be used to better understand the effects of shoreline development on aquatic resources and to evaluate the effects of shoreline development relative to other stressors. Numéro de notice : A2013-389 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.04.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32527
in ISPRS Journal of photogrammetry and remote sensing > vol 81 (July 2013) . - pp 60 - 69[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2013071 RAB Revue Centre de documentation En réserve L003 Disponible Semisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
[article]
Titre : Semisupervised self-learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Immaculada Dopido, Auteur ; Jun Li, Auteur ; Prashanth Reddy Marpu, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4032 - 4044 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] image ROSIS
[Termes IGN] régression logistiqueRésumé : (Auteur) Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine-machine framework when compared with traditional machine-human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the use of self-learning represents an effective and promising strategy in the cont- xt of hyperspectral image classification. Numéro de notice : A2013-374 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2228275 En ligne : https://doi.org/10.1109/TGRS.2012.2228275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32512
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4032 - 4044[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Spectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
[article]
Titre : Spectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery Type de document : Article/Communication Auteurs : Yanfeng Gu, Auteur ; Shizhe Wang, Auteur ; Xiuping Jia, Auteur Année de publication : 2013 Article en page(s) : pp 3968 - 3981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espace de Hilbert
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectraleRésumé : (Auteur) In this paper, we address a spectral unmixing problem for hyperspectral images by introducing multiple-kernel learning (MKL) coupled with support vector machines. To effectively solve issues of spectral unmixing, an MKL method is explored to build new boundaries and distances between classes in multiple-kernel Hilbert space (MKHS). Integrating reproducing kernel Hilbert spaces (RKHSs) spanned by a series of different basis kernels in MKHS is able to provide increased power in handling general nonlinear problems than traditional single-kernel learning in RKHS. The proposed method is developed to solve multiclass unmixing problems. To validate the proposed MKL-based algorithm, both synthetic data and real hyperspectral image data were used in our experiments. The experimental results demonstrate that the proposed algorithm has a strong ability to capture interclass spectral differences and improve unmixing accuracy, compared to the state-of-the-art algorithms tested. Numéro de notice : A2013-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227757 En ligne : https://doi.org/10.1109/TGRS.2012.2227757 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32509
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3968 - 3981[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Band grouping versus band clustering in SVM ensemble classification of hyperspectral imagery / Behnaz Bigdeli in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 6 (June 2013)
[article]
Titre : Band grouping versus band clustering in SVM ensemble classification of hyperspectral imagery Type de document : Article/Communication Auteurs : Behnaz Bigdeli, Auteur ; Farhad Samadzadegan, Auteur ; Peter Reinartz, Auteur Année de publication : 2013 Article en page(s) : pp 523 - 533 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] regroupement de donnéesRésumé : (Auteur) Due to the dense sampling of spectral signatures of land covers, hyperspectral images have a better discrimination among similar ground cover classes than traditional remote sensing data. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral image classification. In addition, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon. Consequently, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifiers in these situations, classifier ensemble system may exhibit better performance. This paper presents a method for classification of hyperspectral data based on two concepts of Band Clustering (BC) and Band Grouping [eg] through a Support Vector machine (SVM) ensemble system. The proposed method uses the BC\BG strategies to split data into few band portions. After this step, we applied SVM on each band cluster\group that is produced in previous step. Finally, Naive Bayes as a classifier fusion method combines the decisions of SVM classifiers. Experimental results show that the proposed method improves the classification accuracy in comparison to the standard SVM and to feature selection methods. Numéro de notice : A2013-362 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.6.523 En ligne : https://doi.org/10.14358/PERS.79.6.523 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32500
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 6 (June 2013) . - pp 523 - 533[article]Change detection from remotely sensed images: From pixel-based to object-based approaches / Masroor Hussain in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)
[article]
Titre : Change detection from remotely sensed images: From pixel-based to object-based approaches Type de document : Article/Communication Auteurs : Masroor Hussain, Auteur ; Dongmei Chen, Auteur ; Angela Cheng, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 91 - 106 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] classification orientée objet
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] exploration de données géographiques
[Termes IGN] télédétectionRésumé : (Auteur) The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection Numéro de notice : A2013-299 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.03.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.03.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32437
in ISPRS Journal of photogrammetry and remote sensing > vol 80 (June 2013) . - pp 91 - 106[article]Exemplaires(1)
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