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Termes IGN > imagerie > image numérique > image optique > image multibande
image multibandeSynonyme(s)Image xs ;Image multispectrale donnees multispectralesVoir aussi |
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Classifying compound structures in satellite images : A compressed representation for fast queries / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
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Titre : Classifying compound structures in satellite images : A compressed representation for fast queries Type de document : Article/Communication Auteurs : Lionel Gueguen, Auteur Année de publication : 2015 Article en page(s) : pp 1803 - 1818 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification orientée objet
[Termes IGN] image multibande
[Termes IGN] image optique
[Termes IGN] occupation du sol
[Termes IGN] segmentation d'imageRésumé : (Auteur) With the increased spatial resolution of current sensor constellations, more details are captured about our changing planet, enabling the recognition of a greater range of land use/land cover classes. While pixeland object-based classification approaches are widely used for extracting information from imagery, recent studies have shown the importance of spatial contexts for discriminating more specific and challenging classes. This paper proposes a new compact representation for the fast query/classification of compound structures from very high resolution optical remote sensing imagery. This bag-of-features representation relies on the multiscale segmentation of the input image and the quantization of image structures pooled into visual word distributions for the characterization of compound structures. A compressed form of the visual word distributions is described, allowing adaptive and fast queries/classification of image patterns. The proposed representation and the query methodology are evaluated for the classification of the UC Merced 21-class data set, for the detection of informal settlements and for the discrimination of challenging agricultural classes. The results show that the proposed representation competes with state-of-the-art techniques. In addition, the complexity analysis demonstrates that the representation requires about 5% of the image storage space while allowing us to perform queries at a speed down to 1 s/ 1000 km2/CPU for 2-m multispectral data. Numéro de notice : A2015-175 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2348864 Date de publication en ligne : 04/09/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2348864 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75894
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 1803 - 1818[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015041 RAB Revue Centre de documentation En réserve L003 Disponible Linear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification / Keng-Hao Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
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Titre : Linear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Keng-Hao Liu, Auteur ; Yen-Yu Lin, Auteur ; Chu-Song Chen, Auteur Année de publication : 2015 Article en page(s) : pp 2254 - 2269 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] classification automatique
[Termes IGN] image hyperspectraleRésumé : (Auteur) Linear spectral mixture analysis (LSMA) has received wide interests for spectral unmixing in the remote sensing community. This paper introduces a framework called multiplekernel learning-based spectral mixture analysis (MKL-SMA) that integrates a newly proposed MKL method into the training process of LSMA. MKL-SMA allows us to adopt a set of nonlinear basis kernels to better characterize the data so that it can enrich the discriminant capability in classification. Because a single kernel is often insufficient to well present all the data characteristics, MKL-SMA has the advantage of providing a broader range of representation flexibilities; it also eases the kernel selection process because the kernel combination parameters can be learned automatically. Unlike most MKL approaches where complex nonlinear optimization problems are involved in their training process, we derived a closed-form solution of the kernel combination parameters in MKL-SMA. Our method is thus efficient for training and easy to implement. The usefulness of MKL-SMA is demonstrated by conducting real hyperspectral image experiments for performance evaluation. Promising results manifest the effectiveness of the proposed MKL-SMA. Numéro de notice : A2015-173 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2358620 Date de publication en ligne : 29/09/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2358620 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75891
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 2254 - 2269[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015041 RAB Revue Centre de documentation En réserve L003 Disponible A physics-based unmixing method to estimate subpixel temperatures on mixed pixels / Manuel Cubero-Castan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
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Titre : A physics-based unmixing method to estimate subpixel temperatures on mixed pixels Type de document : Article/Communication Auteurs : Manuel Cubero-Castan, Auteur ; Jocelyn Chanussot, Auteur ; Véronique Achard, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1894 - 1906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] emissivité
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image TASI
[Termes IGN] méthode des moindres carrés
[Termes IGN] rayonnement infrarouge thermique
[Termes IGN] température de luminanceRésumé : (Auteur) This paper presents a new algorithm for the analysis of linear spectral mixtures in the thermal infrared domain, with the goal to jointly estimate the abundance and the subpixel temperature in a mixed pixel, i.e., to estimate the relative proportion and the temperature of each material composing the mixed pixel. This novel approach is a two-step procedure. First, it estimates the emissivity and the temperature over pure pixels using the standard temperature and emissivity separation (TES) algorithm. Second, it estimates the abundance and the subpixel temperature using a new unmixing physics-based model, called Thermal Remote sensing Unmixing for Subpixel Temperature (TRUST). This model is based on an estimator of the subpixel temperature obtained by linearizing the black body law around the mean temperature of each material. The abundance is then retrieved by minimizing the reconstruction error with the estimation of the subpixel temperatures. The TRUST method is benchmarked on simulated scenes against the fully constrained least squares unmixing applied on the radiance and on the estimation of surface emissivity using the TES algorithm. The TRUST method shows better results on pure and mixed pixels composed of two materials. TRUST also shows promising results when applied on thermal hyperspectral data acquired with the Thermal Airborne Spectrographic Imager during the Detection in Urban scenario using Combined Airborne imaging Sensors campaign and estimates coherent localization of mixed-pixel areas. Numéro de notice : A2015-172 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2350771 Date de publication en ligne : 15/09/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2350771 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75890
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 1894 - 1906[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015041 RAB Revue Centre de documentation En réserve L003 Disponible 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 An 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)
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)
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
PermalinkHyperspectral Band Selection by Multitask Sparsity Pursuit / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
PermalinkSparse unmixing of hyperspectral data using spectral a priori information / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
PermalinkAn abundance characteristic-based independent component analysis for hyperspectral unmixing / Nan Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkAutomatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning / Qian Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkPermalinkExtended random walker-based classification of hyperspectral images / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkExterior orientation of hyperspectral frame images collected with UAV for forest applications / Adilson Berveglieri (2015)
PermalinkExtraction of optimal spectral bands using hierarchical band merging out of hyperspectral data / Arnaud Le Bris (2015)
PermalinkHierarchical unsupervised change detection in multitemporal hyperspectral images / S. Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkHyperspectral image denoising via sparse representation and low-rank constraint / Yong-Qiang Zhao in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
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