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Spatial and spectral image fusion using sparse matrix factorization / Bo Huang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)
[article]
Titre : Spatial and spectral image fusion using sparse matrix factorization Type de document : Article/Communication Auteurs : Bo Huang, Auteur ; Huihui Song, Auteur ; Hengbin Cui, Auteur ; Jigen Peng, Auteur ; Zongben Xu, Auteur Année de publication : 2014 Article en page(s) : pp 1693 - 1704 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse combinatoire (maths)
[Termes IGN] apprentissage automatique
[Termes IGN] factorisation
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Terra-MODIS
[Termes IGN] matrice creuse
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectraleRésumé : (Auteur) In this paper, we present a novel spatial and spectral fusion model (SASFM) that uses sparse matrix factorization to fuse remote sensing imagery with different spatial and spectral properties. By combining the spectral information from sensors with low spatial resolution (LSaR) but high spectral resolution (HSeR) (hereafter called HSeR sensors), with the spatial information from sensors with high spatial resolution (HSaR) but low spectral resolution (LSeR) (hereafter called HSaR sensors), the SASFM can generate synthetic remote sensing data with both HSaR and HSeR. Given two reasonable assumptions, the proposed model can integrate the LSaR and HSaR data via two stages. In the first stage, the model learns from the LSaR data a spectral dictionary containing pure signatures, and in the second stage, the desired HSaR and HSeR data are predicted using the learned spectral dictionary and the known HSaR data. The SASFM is tested with both simulated data and actual Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions, and it is also compared to other representative algorithms. The experimental results demonstrate that the SASFM outperforms other algorithms in generating fused imagery with both the well-preserved spectral properties of MODIS and the spatial properties of ETM+. Generated imagery with simultaneous HSaR and HSeR opens new avenues for applications of MODIS and ETM+. Numéro de notice : A2014-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2253612 En ligne : https://doi.org/10.1109/TGRS.2013.2253612 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33020
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 3 (March 2014) . - pp 1693 - 1704[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Patch-based information reconstruction of cloud-contaminated multitemporal images / Chao-Hung Lin in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)
[article]
Titre : Patch-based information reconstruction of cloud-contaminated multitemporal images Type de document : Article/Communication Auteurs : Chao-Hung Lin, Auteur ; Kang-Hua Lai, Auteur ; Zhi-Bin Chen, Auteur ; Jyun-Yuan Chen, Auteur Année de publication : 2014 Article en page(s) : pp 163 - 174 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] cohérence (physique)
[Termes IGN] corrélation
[Termes IGN] image Landsat-ETM+
[Termes IGN] image multitemporelle
[Termes IGN] manteau neigeuxRésumé : (Auteur) Cloud covers, which are generally present in optical remote sensing images, limit the usage of acquired images and increase the difficulty in data analysis. Thus, information reconstruction of cloud-contaminated images generally plays an important role in image analysis. This paper proposes a novel method to reconstruct cloud-contaminated information in multitemporal remote sensing images. Based on the concept of utilizing temporal correlation of multitemporal images, we propose a patch-based information reconstruction algorithm that spatiotemporally segments a sequence of images into clusters containing several spatially connected components called patches and then clones information from cloud-free and high-similarity patches to their corresponding cloud-contaminated patches. In addition, a seam that passes through homogenous regions is used in information reconstruction to reduce radiometric inconsistency, and information cloning is solved using an optimization process with the determined seam. These processes enable the proposed method to well reconstruct missing information. Qualitative analyses of image sequences acquired by a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor and a quantitative analysis of simulated data with various cloud contamination conditions are conducted to evaluate the proposed method. The experimental results demonstrate the superiority of the proposed method to related methods in terms of radiometric accuracy and consistency, particularly for large clouds in a heterogeneous landscape. Numéro de notice : A2014-035 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2237408 En ligne : https://doi.org/10.1109/TGRS.2012.2237408 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32940
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 1 (January 2014) . - pp 163 - 174[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014011A RAB Revue Centre de documentation En réserve L003 Disponible Restoration 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)
[article]
Titre : Restoration of information obscured by mountainous shadows through Landsat TM/ETM+ images without the use of DEM data : A new method Type de document : Article/Communication Auteurs : Yuan Zhou, Auteur ; Jin Chen, Auteur ; Qinghua Guo, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 313 - 328 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] montagne
[Termes IGN] ombre
[Termes IGN] pixel
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] restauration d'image
[Termes IGN] valeur radiométriqueRésumé : (Auteur) Shadows in remotely sensed imagery occur when objects totally or partially occlude direct light from a source of illumination, generating great difficulty in land cover interpretation and classification because of the loss of spectral information of shaded pixels. In a mountainous environment with rough terrain, shadows are especially pronounced due to the differentiation of direct illumination between sunny and shady slopes. Topographic correction methods, which are widely used to adjust for differences in solar incidence angles, can partly alleviate the impacts of shadows. However, there are two limitations: one is that the contemporary topographic corrections have little effect on areas that have very low incidence angles and areas that are completely without direct solar illumination (cast shadow); another is that their effectiveness is restricted by the data quality and completeness, spatial resolution, and elevation accuracy of the Digital Elevation Model (DEM) data, which is not currently available in all parts of the world. Thus, noise and errors may be introduced in topographic correction during resampling and geometric registration of the target image. This paper proposes a new approach to restore the radiometric information of mountainous cast shadows using a spectral processing technique called “continuum removal” (CR) without the aid of DEM. The CR-based approach makes full use of the spectral information derived from both the shaded pixels and their neighboring nonshaded pixels of the same land cover type. Several Landsat TM images were used to assess the performance of the proposed method. Results indicated that the proposed method can effectively restore the spectral values of shaded pixels more accurately than the ATCOR_3 correction method, especially for very low incidence angle areas and cast shadows. By comparing data values of shaded pixels with nonshaded pixels (pure reference pixels) of their same class, images processed by the proposed method had the lowest average root mean square error (RMSE) between them in visible, NIR and SWIR bands, followed by the ATCOR_3 correction method and the original image. In addition, the proposed method achieved the best classification accuracy, higher than those from the original test image and the ATCOR_3 corrected image generated using 90 m or 30 m spatial resolution DEM. Therefore, the Continuum Removal method is a better alternative for restoring objects obscured by mountainous shadow when adequate DEM data are unavailable and the quality of DEM cannot satisfy the requirements of topographic correction algorithms. Numéro de notice : A2014-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2239651 En ligne : https://doi.org/10.1109/TGRS.2013.2239651 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32942
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 1 (January 2014) . - pp 313 - 328[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014011A RAB Revue Centre de documentation En réserve L003 Disponible An entropy-based multispectral image classification algorithm / Di Long in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)
[article]
Titre : An entropy-based multispectral image classification algorithm Type de document : Article/Communication Auteurs : Di Long, Auteur ; Vijay P. Singh, Auteur Année de publication : 2013 Article en page(s) : pp 5225 - 5238 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] carte d'occupation du sol
[Termes IGN] classificateur
[Termes IGN] entropie maximale
[Termes IGN] Houston (Texas)
[Termes IGN] image Landsat-ETM+Résumé : (Auteur) Employing the entropy theory, this paper presents a new and robust multispectral image classification algorithm. The digital number (DN) in remotely sensed multispectral images is considered as a random variable when judging the allocation of unknown pixels into predefined training classes. If an unknown pixel shows a similar DN vector as the pixels in a training class, it will increase the global entropy defined as the sum of DN probabilities multiplied by the logarithm of DN probabilities for all pixels within the training class. The unknown pixel is to be assigned to the class for which the entropy of the training class is increased most due to the inclusion of the pixel. The proposed entropy-based classification (EC) is compared with the maximum likelihood classification (MLC), parallelepiped classification, minimum distance classification, Mahalanobis distance classification (MDC), iterative self-organizing data analysis technique (ISODATA) classification, and K-means classification. These classifiers were applied to a Landsat Enhanced Thematic Mapper Plus image covering Houston, Texas, USA, acquired on October 16, 1999. A reference land cover map from the National Land Cover Data 2001 of the same area was taken as a ground reference to assess the accuracy of classification results, suggesting that the EC showed comparable overall accuracy as MDC, and they both outperformed other classifiers. The results of MLC can be improved by substituting the multivariate lognormal or gamma distribution for the multivariate normal distribution involved in its assumption. The EC algorithm has the potential to produce reliable land cover maps regardless of the distribution of DN vectors and relevant parameters of probability density functions involved in other classifiers. Numéro de notice : A2013-694 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2272560 En ligne : https://doi.org/10.1109/TGRS.2013.2272560 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32830
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 12 (December 2013) . - pp 5225 - 5238[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013121 RAB Revue Centre de documentation En réserve L003 Disponible A semi-ellipsoid-model based fuzzy classifier to map grassland in Inner Mongolia, China / Hai Lan in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)
[article]
Titre : A semi-ellipsoid-model based fuzzy classifier to map grassland in Inner Mongolia, China Type de document : Article/Communication Auteurs : Hai Lan, Auteur ; Yichun Xie, Auteur Année de publication : 2013 Article en page(s) : pp 21 - 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification floue
[Termes IGN] classification hybride
[Termes IGN] fusion d'images
[Termes IGN] image CBERS
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] prairieRésumé : (Auteur) Remote sensing techniques offer effective means for mapping plant communities. However, mapping grassland with fine vegetative classes over large areas has been challenging for either the coarse resolutions of remotely sensed images or the high costs of acquiring images with high-resolutions. An improved hybrid-fuzzy-classifier (HFC) derived from a semi-ellipsoid-model (SEM) is developed in this paper to achieve higher accuracy for classifying grasslands with Landsat images. The Xilin River Basin, Inner Mongolia, China, is chosen as the study area, because an acceptable volume of ground truthing data was previously collected by multiple research communities. The accuracy assessment is based on the comparison of the classification outcomes from four types of image sets: (1) Landsat ETM+ August 14, 2004, (2) Landsat TM August 12, 2009, (3) the fused images of ETM+ with CBERS, and (4) TM with CBERS, respectively, and by three classifiers, the proposed HFC-SEM, the tetragonal pyramid model (TPM) based HFC, and the support vector machine method. In all twelve classification experiments, the HFC-SEM classifier had the best overall accuracy statistics. This finding indicates that the medium resolution Landsat images can be used to map grassland vegetation with good vegetative detail when the proper classifier is applied. Numéro de notice : A2013-605 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.07.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.07.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32741
in ISPRS Journal of photogrammetry and remote sensing > vol 85 (November 2013) . - pp 21 - 31[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013111 RAB Revue Centre de documentation En réserve L003 Disponible A spectral gradient difference based approach for land cover change detection / Jun Chen in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)PermalinkApport de la télédétection à l'analyse de la dynamique de l'occupation du sol à partir d'une utilisation couplée d'un modèle de markov et d'un automate cellulaire. Cas du département de Sintra (Centre-Ouest de la Cote d'Ivoire). / Vami Hermann N'guessan Bi in Revue Française de Photogrammétrie et de Télédétection, n° 204 (Octobre 2013)PermalinkLe bassin versant du Mayo-Tsagana (Nord Cameroun) : un bassin versant expérimental pour une compréhension des relations homme/milieu / Louise Leroux in Revue Française de Photogrammétrie et de Télédétection, n° 202 (Avril 2013)PermalinkMultitemporal cross-calibration of the Terra MODIS and Landsat 7 ETM+ reflective solar bands / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)PermalinkAssessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study / Gyanesh Chander in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 1 (March 2013)PermalinkIn-situ transfer standard and coincident-view intercomparisons for sensor cross-calibration / Kurt Thome in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 1 (March 2013)PermalinkIntegrating Landsat-7 imagery with physics-based models for quantitative mapping of coastal waters near river discharges / Nima Pahlevan in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 11 (November 2012)PermalinkApports de la typologie du réseau hydrographique et de la photo-interprétation de l'imagerie Landsat ETM+ pour l'homogénéisation de la couverture des cartes géologiques : application à quelques exemples dans le sud de la Tunisie / Mehdi Ben Hassen in Photo interprétation, European journal of applied remote sensing, vol 48 n° 3 (septembre 2012)PermalinkTopographic corrections of satellite data for regional monitoring / S. Goslee in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 9 (September 2012)PermalinkA variational gradient-based fusion method for visible and SWIR imagery / H. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 9 (September 2012)Permalink