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image multibandeSynonyme(s)Image xs ;Image multispectrale donnees multispectralesVoir aussi |
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Supervised 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)
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Titre : Supervised spectral–spatial hyperspectral image classification with weighted markov random fields Type de document : Article/Communication Auteurs : Le Sun, Auteur ; Zebin Wu, Auteur ; Jianjun Liu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1490 - 1503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectrale
[Termes IGN] pondération
[Termes IGN] régression logistiqueRésumé : (Auteur) This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial information. Under the maximum a posteriori framework, we propose a supervised classification model which includes a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm, named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic. Numéro de notice : A2015-134 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2344442 Date de publication en ligne : 18/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2344442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75800
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1490 - 1503[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Coregistration 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)
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Titre : Coregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information Type de document : Article/Communication Auteurs : Janja Avbelj, Auteur ; Dorota Iwaszczuk, Auteur ; Rupert Müller, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 23 - 34 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] appariement d'images
[Termes IGN] classification orientée objet
[Termes IGN] détection du bâti
[Termes IGN] données multicapteurs
[Termes IGN] image hyperspectrale
[Termes IGN] milieu urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] superposition d'imagesRésumé : (auteur) For image fusion in remote sensing applications the georeferencing accuracy using position, attitude, and camera calibration measurements can be insufficient. Thus, image processing techniques should be employed for precise coregistration of images. In this article a method for multimodal object-based image coregistration refinement between hyperspectral images (HSI) and digital surface models (DSM) is presented. The method is divided in three parts: object outline detection in HSI and DSM, matching, and determination of transformation parameters. The novelty of our proposed coregistration refinement method is the use of material properties and height information of urban objects from HSI and DSM, respectively. We refer to urban objects as objects which are typical in urban environments and focus on buildings by describing them with 2D outlines. Furthermore, the geometric accuracy of these detected building outlines is taken into account in the matching step and for the determination of transformation parameters. Hence, a stochastic model is introduced to compute optimal transformation parameters. The feasibility of the method is shown by testing it on two aerial HSI of different spatial and spectral resolution, and two DSM of different spatial resolution. The evaluation is carried out by comparing the accuracies of the transformations parameters to the reference parameters, determined by considering object outlines at much higher resolution, and also by computing the correctness and the quality rate of the extracted outlines before and after coregistration refinement. Results indicate that using outlines of objects instead of only line segments is advantageous for coregistration of HSI and DSM. The extraction of building outlines in comparison to the line cue extraction provides a larger amount of assigned lines between the images and is more robust to outliers, i.e. false matches. Numéro de notice : A2015-051 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.05.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.05.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75237
in ISPRS Journal of photogrammetry and remote sensing > vol 100 (February 2015) . - pp 23 - 34[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Gabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
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Titre : Gabor feature-based collaborative representation for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Linlin Shen, Auteur ; Qingquan Li, Auteur Année de publication : 2015 Article en page(s) : pp 1118 - 1129 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification spectrale
[Termes IGN] conception collaborative
[Termes IGN] état de l'art
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classificationRésumé : (Auteur) Sparse-representation-based classification (SRC) assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, which has successfully been applied to several pattern recognition problems. According to compressive sensing theory, the l1-norm minimization could yield the same sparse solution as the l0 norm under certain conditions. However, the computational complexity of the l1-norm optimization process is often too high for large-scale high-dimensional data, such as hyperspectral imagery (HSI). To make matter worse, a large number of training data are required to cover the whole sample space, which is difficult to obtain for hyperspectral data in practice. Recent advances have revealed that it is the collaborative representation but not the l1-norm sparsity that makes the SRC scheme powerful. Therefore, in this paper, a 3-D Gabor feature-based collaborative representation (3GCR) approach is proposed for HSI classification. When 3-D Gabor transformation could significantly increase the discrimination power of material features, a nonparametric and effective l2-norm collaborative representation method is developed to calculate the coefficients. Due to the simplicity of the method, the computational cost has been substantially reduced; thus, all the extracted Gabor features can be directly utilized to code the test sample, which conversely makes the l2-norm collaborative representation robust to noise and greatly improves the classification accuracy. The extensive experiments on two real hyperspectral data sets have shown higher performance of the proposed 3GCR over the state-of-the-art methods in the literature, in terms of both the classifier complexity and generalization ability from very small training sets. Numéro de notice : A2015-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2334608 En ligne : 10.1109/TGRS.2014.2334608 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75624
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 1118 - 1129[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015021 RAB Revue Centre de documentation En réserve L003 Disponible vol 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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[n° ou bulletin]
est un bulletin de ISPRS Journal of photogrammetry and remote sensing / International society for photogrammetry and remote sensing (1980 -) (1990 -)
Titre : vol 100 - February 2015 - High-resolution Earth imaging for geospatial information Type de document : Périodique Auteurs : Christian Heipke, Éditeur scientifique ; Uwe Soergel, Éditeur scientifique ; Franz Rottensteiner, Éditeur scientifique ; Boris Jutzi, Éditeur scientifique Année de publication : 2015 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] façade
[Termes IGN] image hyperspectrale
[Termes IGN] MicMac
[Termes IGN] réseau de surveillance géophysiqueNuméro de notice : 081-201502 Affiliation des auteurs : non IGN Nature : Numéro de périodique En ligne : http://www.sciencedirect.com/science/journal/09242716/100 Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=24835 [n° ou bulletin] Contient
- In-flight photogrammetric camera calibration and validation via complementary lidar / A.S. Gneeniss in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
- Coregistration 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)
- Radiometric and geometric evaluation of GeoEye-1, WorldView-2 and Pléiades-1A stereo images for 3D information extraction / Daniela Poli in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
- Measurement of ground displacement from optical satellite image correlation using the free open-source software MicMac / Ana-Maria Rosu in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
- Evaluation and comparison of different radargrammetric approaches for Digital Surface Models generation from COSMO-SkyMed, TerraSAR-X, RADARSAT-2 imagery: Analysis of Beauport (Canada) test site / P. Capaldo in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
- Measuring thermal expansion using X-band persistent scatterer interferometry / Michele Crosetto in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
- Persistent scatterers at building facades – Evaluation of appearance and localization accuracy / Stefan Gernhardt in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
- Integrating SAR and derived products into operational volcano monitoring and decision support systems / Franz J. Meyer in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
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Code-barres Cote Support Localisation Section Disponibilité 081-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral Band Selection by Multitask Sparsity Pursuit / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
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Titre : Hyperspectral Band Selection by Multitask Sparsity Pursuit Type de document : Article/Communication Auteurs : Yuan Yuan, Auteur ; Qi Wang, Auteur Année de publication : 2015 Article en page(s) : pp 631 -644 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] bande spectrale
[Termes IGN] classification multibande
[Termes IGN] couleur à l'écran
[Termes IGN] image hyperspectrale
[Termes IGN] visualisation de donnéesRésumé : (Auteur) Hyperspectral images have been proved to be effective for a wide range of applications; however, the large volume and redundant information also bring a lot of inconvenience at the same time. To cope with this problem, hyperspectral band selection is a pertinent technique, which takes advantage of removing redundant components without compromising the original contents from the raw image cubes. Because of its usefulness, hyperspectral band selection has been successfully applied to many practical applications of hyperspectral remote sensing, such as land cover map generation and color visualization. This paper focuses on groupwise band selection and proposes a new framework, including the following contributions: 1) a smart yet intrinsic descriptor for efficient band representation; 2) an evolutionary strategy to handle the high computational burden associated with groupwise-selection-based methods; and 3) a novel MTSP-based criterion to evaluate the performance of each candidate band combination. To verify the superiority of the proposed framework, experiments have been conducted on both hyperspectral classification and color visualization. Experimental results on three real-world hyperspectral images demonstrate that the proposed framework can lead to a significant advancement in these two applications compared with other competitors. Numéro de notice : A2015-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2326655 En ligne : https://doi.org/10.1109/TGRS.2014.2326655 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75621
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 631 -644[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Sparse 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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