Descripteur
Documents disponibles dans cette catégorie (1779)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
An inquiry on contrast enhancement methods for satellite images / Jose-Luis Lisani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
![]()
[article]
Titre : An inquiry on contrast enhancement methods for satellite images Type de document : Article/Communication Auteurs : Jose-Luis Lisani, Auteur ; Julien Michel, Auteur ; Jean-Michel Morel, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7044 - 7054 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] amélioration du contraste
[Termes IGN] couleur à l'écran
[Termes IGN] détection d'ombre
[Termes IGN] intensité lumineuseRésumé : (Auteur) Enhancement algorithms are absolutely necessary for the visualization of both shadowed and bright image regions. Defining algorithms that permit to visualize them simultaneously without altering the image content is therefore extremely relevant for remote sensing applications. In this paper, we present the results of two successive benchmarks which tested the performance of the state-of-the-art contrast enhancement and benchmarks algorithms applied to satellite images. Experts from the French Space Agency Centre National d'Etudes Spatiales (CNES), Service Régional de Traitement d'Image et de Télédétection (SERTIT), and two European universities assessed the quality and fidelity of the results of several state-of-the-art enhancement algorithms on the excerpts from seven images (five Pleiades and two simulated 30-cm images). The first benchmark permitted to tighten the procedure and the selection of the test images for the second one, and to make a first selection of concurrent algorithms. The second benchmark not only included the best algorithms selected by the first benchmark but also added even more competitors in the tone-mapping class. The results of both benchmarks were coherent. They point a particular retinex-based algorithm as the best compromise between the competitive requirements of a contrast enhancement in dark regions and a preservation of detail in bright parts. Numéro de notice : A2016-924 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2594339 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2594339 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83328
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7044 - 7054[article]An integrated framework for the spatio–temporal–spectral fusion of remote sensing images / Huanfeng Shen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
![]()
[article]
Titre : An integrated framework for the spatio–temporal–spectral fusion of remote sensing images Type de document : Article/Communication Auteurs : Huanfeng Shen, Auteur ; Xiangchao Meng, Auteur ; Liangpei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 7135 - 7148 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de fusion
[Termes IGN] données spatiotemporelles
[Termes IGN] fusion d'images
[Termes IGN] fusion de données multisource
[Termes IGN] image spectraleRésumé : (Auteur) Remote sensing satellite sensors feature a tradeoff between the spatial, temporal, and spectral resolutions. In this paper, we propose an integrated framework for the spatio-temporal-spectral fusion of remote sensing images. There are two main advantages of the proposed integrated fusion framework: it can accomplish different kinds of fusion tasks, such as multiview spatial fusion, spatio-spectral fusion, and spatio-temporal fusion, based on a single unified model, and it can achieve the integrated fusion of multisource observations to obtain high spatio-temporal-spectral resolution images, without limitations on the number of remote sensing sensors. The proposed integrated fusion framework was comprehensively tested and verified in a variety of image fusion experiments. In the experiments, a number of different remote sensing satellites were utilized, including IKONOS, the Enhanced Thematic Mapper Plus (ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Hyperspectral Digital Imagery Collection Experiment (HYDICE), and Système Pour l' Observation de la Terre-5 (SPOT-5). The experimental results confirm the effectiveness of the proposed method. Numéro de notice : A2016-926 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2596290 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2596290 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83332
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7135 - 7148[article]An iterative interpolation deconvolution algorithm for superresolution land cover mapping / Feng Ling in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
![]()
[article]
Titre : An iterative interpolation deconvolution algorithm for superresolution land cover mapping Type de document : Article/Communication Auteurs : Feng Ling, Auteur ; Giles M. Foody, Auteur ; Yong Ge, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7210 - 7222 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification du maximum a posteriori
[Termes IGN] déconvolution
[Termes IGN] image à ultra haute résolution
[Termes IGN] itérationRésumé : (Auteur) Superresolution mapping (SRM) is a method to produce a fine-spatial-resolution land cover map from coarse-spatial-resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation and then determines class labels of fine-resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between the observed coarse-resolution fraction images and the latent fine-resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms and should be replaced by deconvolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation deconvolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse-resolution fraction images with an area-to-area interpolation algorithm and produces an initial fine-resolution land cover map by deconvolution. The fine-spatial-resolution land cover map is then updated by reconvolution, back-projection, and deconvolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multispectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors and can preserve the patch continuity and the patch boundary smoothness simultaneously. Moreover, the IID algorithm produced fine-resolution land cover maps with higher accuracies than those produced by other SRM algorithms. Numéro de notice : A2016-928 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598534 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2598534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83342
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7210 - 7222[article]Automated co-registration of satellite images through luminance transformation / Deniz Gerçek in Photogrammetric record, vol 31 n° 156 (December 2016 - February 2017)
![]()
[article]
Titre : Automated co-registration of satellite images through luminance transformation Type de document : Article/Communication Auteurs : Deniz Gerçek, Auteur ; Davut Çeşmeci, Auteur ; M. Kemal Güllü, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 407 - 427 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] caméra numérique
[Termes IGN] image satellite
[Termes IGN] intensité lumineuse
[Termes IGN] partitionnement par bloc
[Termes IGN] radianceRésumé : (auteur) This paper presents an extensive evaluation of a novel intensity-based image co-registration method called luminance transformed differences (LTD) which is compared with current alternatives. The proposed method suggests the use of the luminance transform operation (LTO) to reduce luminance differences between images that may well affect image registration accuracy. Registration is implemented in a block-based fashion in order to achieve a non-rigid transformation at a sub-pixel accuracy level. Sample images from different sensors, exhibiting various geometric distortions, are registered to test the various intensity-based image registration alternatives. LTD is found to outperform other methods; its high accuracy can be achieved even using small searching blocks which substantially reduce the computational cost. Numéro de notice : A2016--004 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12160 Date de publication en ligne : 04/10/2016 En ligne : https://doi.org/10.1111/phor.12160 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83822
in Photogrammetric record > vol 31 n° 156 (December 2016 - February 2017) . - pp 407 - 427[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 106-2016041 RAB Revue Centre de documentation En réserve L003 Disponible Bundle adjustment of spherical images acquired with a portable panoramic image mapping system (PPIMS) / Yi-Hsing Tseng in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 12 (December 2016)
![]()
[article]
Titre : Bundle adjustment of spherical images acquired with a portable panoramic image mapping system (PPIMS) Type de document : Article/Communication Auteurs : Yi-Hsing Tseng, Auteur ; Yung-Chuan Chen, Auteur ; Kuan-Ying Lin, Auteur Année de publication : 2016 Article en page(s) : pp 935 - 943 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] antenne GPS
[Termes IGN] compensation par faisceaux
[Termes IGN] image panoramique
[Termes IGN] prise de vue terrestre
[Termes IGN] spatiotriangulation
[Termes IGN] système de numérisation mobileRésumé : (auteur) Thanks to the development of mobile mapping technologies, close-range photogrammetry (CRP) has advanced to be an efficient mapping method for a variety of applications. A compact CRP system equipped with multiple cameras and a GPS receiver is one of those advanced portable mapping systems. A portable panoramic image mapping system (PPIMS) was specially designed to capture panoramic images with eight cameras and to obtain the position of image station with a GPS receiver. A PPIMS can be considered as a panoramic CRP system. The coordinates of an object point can be determined by the intersection of panoramic image points. For the implementation, we propose a new concept of photogrammetry by using panoramic images. Eight images captured by PPIMS forms a spherical panorama image (SPI). Instead of using the original images, PPIMS SPIs are then used for photogrammetric triangulation and mapping. Under this circumstance, one SPI is formed for each station, and it is associated with only one set of exterior orientation (EO) parameters. Traditional collinearity equations are not applicable to SPI triangulation and mapping. Therefore, a novel bundle adjustment algorithm is proposed to solve EO of multi-station SPIs. Because PPIMS SPIs are not ideal SPIs, a correction scheme was also developed to correct the imperfect geometry of PPIMS SPI. Two test studies were performed for the data collected at a campus test field of National Cheng Kung University (NCKU) and at a historical site of Tainan. Both cases demonstrate the feasibility of SPI bundle adjustment and applying corrections for PPIMS SPIs necessary for effective for bundle adjustment. Furthermore, the experiment's results also confirm that SPIs can replace original images for PPIMS triangulation. Numéro de notice : A2016-982 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.12.935 En ligne : https://doi.org/10.14358/PERS.82.12.935 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83698
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 12 (December 2016) . - pp 935 - 943[article]Class-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
PermalinkDictionary learning for promoting structured sparsity in hyperspectral compressive sensing / Lei Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
PermalinkHierarchical and adaptive phase correlation for precise disparity estimation of UAV images / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
PermalinkMultiband image fusion based on spectral unmixing / Qi Wei in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
PermalinkA robust background regression based score estimation algorithm for hyperspectral anomaly detection / Zhao Rui in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)
PermalinkBlind hyperspectral unmixing using total variation and ℓq sparse regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
PermalinkFast three-dimensional empirical mode decomposition of hyperspectral images for class-oriented multitask learning / Zhi He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
PermalinkMultiple kernel learning based on discriminative kernel clustering for hyperspectral band selection / Jie Feng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
PermalinkRobust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification / Zhi He in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)
PermalinkSemi-supervised hyperspectral classification from a small number of training samples using a co-training approach / Michał Romaszewski in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)
PermalinkA Computationally efficient algorithm for fusing multispectral and hyperspectral images / Raúl Guerra in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkDeep feature extraction and classification of hyperspectral images based on convolutional neural networks / Yushi Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkDistributed texture-based land cover classification algorithm using hidden Markov model for multispectral data / S. Jenicka in Survey review, vol 48 n° 351 (October 2016)
PermalinkEvaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)
PermalinkImage processing and GIS techniques applied to high resolution satellite data for lineament mapping of thermal power plant site in Allahabad district, U.P., India / Aniruddha Uniyal in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)
PermalinkObject-based morphological profiles for classification of remote sensing imagery / Christian Geiss in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkA probabilistic approach to detect mixed periodic patterns from moving object data / Jun Li in Geoinformatica, vol 20 n° 4 (October - December 2016)
PermalinkRobust collaborative nonnegative matrix factorization for hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkSemisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning / Xiaorui Ma in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)
PermalinkA tensor decomposition-based anomaly detection algorithm for hyperspectral image / Xing Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
Permalink