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Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning / X. Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 7086 - 7098 Titre : Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning Type de document : Article/Communication Auteurs : X. Li, Auteur ; H. Shen, Auteur ; L. Zhang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 7086 - 7098 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage (cognition) [Termes IGN] détection d'ombre [Termes IGN] réflectance [Termes IGN] température de surface Résumé : (Auteur) With regard to quantitative remote sensing products in the visible and infrared ranges, thick clouds and accompanying shadows are an inevitable source of noise. Due to the absence of adequate supporting information from the data themselves, it is a formidable challenge to accurately restore the surficial information underlying large-scale clouds. In this paper, dictionary learning is expanded into the multitemporal recovery of quantitative data contaminated by thick clouds and shadows. This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts. In order to make better use of the temporal correlations, the expanded KSVD algorithm seeks an optimized temporal path, and the expanded Bayesian method adaptively weights the temporal correlations. In the experiments, the proposed algorithms are applied to a reflectance product and a land surface temperature product, and the respective advantages of the two algorithms are investigated. The results show that, from both the qualitative visual effect and the quantitative objective evaluation, the proposed methods are effective. Numéro de notice : A2014-543 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2307354 En ligne : https://doi.org/10.1109/TGRS.2014.2307354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74160 [article]

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Hyperspectral image denoising employing a spectral-spatial adaptive total variation model / Q. Yuan in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 1 (October 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 10 Tome 1 (October 2012) . - pp 3660 - 3677 Titre : Hyperspectral image denoising employing a spectral-spatial adaptive total variation model Type de document : Article/Communication Auteurs : Q. Yuan, Auteur ; L. Zhang, Auteur ; H. Shen, Auteur Année de publication : 2012 Article en page(s) : pp 3660 - 3677 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] itération [Termes IGN] variation Résumé : (Auteur) The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands. Meanwhile, in the same band, there exist different spatial property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different spatial property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-spatial adaptive total variation (TV) model, in which the spectral noise differences and spatial information differences are both considered in the process of noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-spatial adaptive mechanism in the denoising process, and superior denoising results are produced. Numéro de notice : A2012-523 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2185054 Date de publication en ligne : 07/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2185054 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31969 [article]

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A variational gradient-based fusion method for visible and SWIR imagery / H. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 9 (September 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 9 (September 2012) . - pp 947 - 958 Titre : A variational gradient-based fusion method for visible and SWIR imagery Type de document : Article/Communication Auteurs : H. Li, Auteur ; L. Zhang, Auteur ; H. Shen, Auteur ; P. Li, Auteur Année de publication : 2012 Article en page(s) : pp 947 - 958 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] débrumage [Termes IGN] effet atmosphérique [Termes IGN] fusion d'images [Termes IGN] gradient [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-SWIR [Termes IGN] longueur d'onde Résumé : (Auteur) This paper presents a new variational gradient-based fusion method for visible and short-wave infrared (swm) imagery. The proposed method enables spatial enhancement and dehazing of visible imagery. Integrating gradients from SWIR imagery into visible imagery produces a single image with true color and sharp gradients. A constraint based on band correlation is included to improve the enhancement and implement dehazing. The band correlation is according to the quantitative relationship between the wavelength and the atmospheric effect caused by Rayleigh scattering. In this study, both clear and hazy Landsat ETM+ images are used in the experiments. By visual assessment, the gradient of the fused image is more salient than that of the original image, and the true color is well preserved. With the inclusion of the band correlation constraint, the proposed fusion method yields almost haze-free results. Quantitatively, the Metric Q of the fused images is significantly higher than that of the original images; the largest increase of the Metric Q in the experimental results is from 0.0114 to 0.0611. Moreover, for the results of the proposed method, the Metric Q increase in the visible bands declines from blue band to red band. Numéro de notice : A2012-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.9.947 En ligne : https://doi.org/10.14358/PERS.78.9.947 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31888 [article]