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A technique for simultaneous visualization and segmentation of hyperspectral data / Abhimitra Meka in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
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Titre : A technique for simultaneous visualization and segmentation of hyperspectral data Type de document : Article/Communication Auteurs : Abhimitra Meka, Auteur ; Subhasis Chaudhuri, Auteur Année de publication : 2015 Article en page(s) : pp 1707 - 1717 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image hyperspectrale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] redondance de données
[Termes IGN] segmentation d'image
[Termes IGN] visualisation simultanéeRésumé : (Auteur) In this paper, we propose an optimization-based method for simultaneous fusion and unsupervised segmentation of hyperspectral remote sensing images by exploiting redundancy in the data. The hyperspectral data set is visualized as a single image obtained by weighted addition of all spectral points at each pixel location in the data set. The weights are optimized to improve those statistical characteristics of the fused image, which invoke an enhanced response from a human observer. A piecewise-constant smoothness constraint is imposed on the weights instead of the fused image by minimization of its 3-D total-variation norm, thus preventing the fused image from blurring. The optimal recovery of the weight matrix additionally provides useful information in segmenting the hyperspectral data set spatially. We provide ample experimental results to substantiate the usefulness of the proposed method. Numéro de notice : A2015-170 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2346653 Date de publication en ligne : 04/09/2014 En ligne : https://doi.org/04/09/201410.1109/TGRS.2014.2346653 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75886
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 1707 - 1717[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015041 RAB Revue Centre de documentation En réserve L003 Disponible Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery / Lei Ma in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
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Titre : Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery Type de document : Article/Communication Auteurs : Lei Ma, Auteur ; Liang Cheng, Auteur ; Manchung Li, Auteur ; Yongxue Liu, Auteur ; Xiaoxue Ma, Auteur Année de publication : 2015 Article en page(s) : pp 14 - 27 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification orientée objet
[Termes IGN] drone
[Termes IGN] échelle de prise de vue
[Termes IGN] image à ultra haute résolution
[Termes IGN] image aérienne
[Termes IGN] image optique
[Termes IGN] taille du jeu de donnéesRésumé : (auteur) Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class. Numéro de notice : A2015-692 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.12.026 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.12.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78323
in ISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 14 - 27[article]Contextual classification of point cloud data by exploiting individual 3d neigbourhoods / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)
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Titre : Contextual classification of point cloud data by exploiting individual 3d neigbourhoods Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; A. Schmidt, Auteur ; Clément Mallet , Auteur ; Stefan Hinz, Auteur ; Franz Rottensteiner, Auteur ; Boris Jutzi, Auteur
Année de publication : 2015 Conférence : ISPRS 2015, PIA 2015 - HRIGI 2015 Joint ISPRS conference 25/03/2015 27/03/2015 Munich Allemagne ISPRS OA Annals Article en page(s) : pp 271 - 278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification contextuelle
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] scène urbaine
[Termes IGN] semis de points
[Termes IGN] voisinage (relation topologique)
[Termes IGN] zone urbaineRésumé : (auteur) The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification. Numéro de notice : A2015--052 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-II-3-W4-271-2015 Date de publication en ligne : 11/03/2015 En ligne : http://dx.doi.org/10.5194/isprsannals-II-3-W4-271-2015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82698
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol II-3 W4 (March 2015) . - pp 271 - 278[article]Documents numériques
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Contextual classification of point cloud data ... - pdf éditeurAdobe Acrobat PDFAn adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
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Titre : An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery Type de document : Article/Communication Auteurs : Yanfei Zhong, Auteur ; Yunyun Wu, Auteur ; Xiong Xu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1411 - 1426 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse infrapixellaire
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] classification du maximum a posteriori
[Termes IGN] classification pixellaire
[Termes IGN] image hyperspectrale
[Termes IGN] traitement automatique d'images
[Termes IGN] traitement de données localiséesRésumé : (Auteur) The subpixel mapping technique can specify the spatial distribution of different categories at the subpixel scale by converting the abundance map into a higher resolution image, based on the assumption of spatial dependence. Traditional subpixel mapping algorithms only utilize the low-resolution image obtained by the classification image downsampling and do not consider the spectral unmixing error, which is difficult to account for in real applications. In this paper, to improve the accuracy of the subpixel mapping, an adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery. In AMCDSM, to better simulate a real remote sensing scene, the low-resolution abundance images are obtained by the spectral unmixing method from the downsampled original image or real low-resolution images. The MAP model is extended by considering the spatial prior models (Laplacian, total variation (TV), and bilateral TV) to obtain the high-resolution subpixel distribution map. To avoid the setting of the regularization parameter, an adaptive parameter selection method is designed to acquire the optimal subpixel mapping results. In addition, in AMCDSM, to take into account the spectral unmixing error in real applications, a winner-take-all strategy is proposed to achieve a better subpixel mapping result. The proposed method was tested on simulated, synthetic, and real hyperspectral images, and the experimental results demonstrate that the AMCDSM algorithm outperforms the traditional subpixel mapping methods and provides a simple and efficient algorithm to regularize the ill-posed subpixel mapping problem. Numéro de notice : A2015-132 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2340734 Date de publication en ligne : 07/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2340734 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75796
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1411 - 1426[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Collaborative representation for hyperspectral anomaly detection / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
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Titre : Collaborative representation for hyperspectral anomaly detection Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Qian Du, Auteur Année de publication : 2015 Article en page(s) : pp 1463 - 1474 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification pixellaire
[Termes IGN] détection d'anomalie
[Termes IGN] distance pondérée
[Termes IGN] image hyperspectrale
[Termes IGN] matrice
[Termes IGN] optimisation (mathématiques)
[Termes IGN] plus proche voisin, algorithme duRésumé : (Auteur) In this paper, collaborative representation is proposed for anomaly detection in hyperspectral imagery. The algorithm is directly based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. The representation is assumed to be the linear combination of neighboring pixels, and the collaboration of representation is reinforced by l2-norm minimization of the representation weight vector. To adjust the contribution of each neighboring pixel, a distance-weighted regularization matrix is included in the optimization problem, which has a simple and closed-form solution. By imposing the sum-to-one constraint to the weight vector, the stability of the solution can be enhanced. The major advantage of the proposed algorithm is the capability of adaptively modeling the background even when anomalous pixels are involved. A kernel extension of the proposed approach is also studied. Experimental results indicate that our proposed detector may outperform the traditional detection methods such as the classic Reed-Xiaoli (RX) algorithm, the kernel RX algorithm, and the state-of-the-art robust principal component analysis based and sparse-representation-based anomaly detectors, with low computational cost. Numéro de notice : A2015-133 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2343955 Date de publication en ligne : 12/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2343955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75799
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1463 - 1474[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Comparaison de méthodes de recalage d'images pour le géoréférencement de clichés anciens / Leïla Bakkouch in XYZ, n° 142 (mars - mai 2015)
PermalinkConstrained least squares algorithms for nonlinear unmixing of hyperspectral imagery / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
PermalinkDéveloppement d'un logiciel de calcul de trajectoire pour un drone / Valerio Baiocchi in Géomatique expert, n° 103 (mars - avril 2015)
PermalinkEmploying ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland / Samuel Adelabu in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)
PermalinkLes journées de la recherche 2015 à l'IGN / Anonyme in Géomatique expert, n° 103 (mars - avril 2015)
PermalinkSemisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization / Zhangyang Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
PermalinkSupervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
PermalinkCoregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information / Janja Avbelj in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
PermalinkGabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
Permalinkvol 100 - February 2015 - High-resolution Earth imaging for geospatial information (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Christian Heipke
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