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An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data / Michael Kwok-Po Ng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 6 (June 2017) . - pp 3367 - 3381 Titre : An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data Type de document : Article/Communication Auteurs : Michael Kwok-Po Ng, Auteur ; Qiangqiang Yuan, Auteur ; Li Yan, Auteur ; Jing Sun, Auteur Année de publication : 2017 Article en page(s) : pp 3367 - 3381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] détection de partie cachée [Termes IGN] données spatiotemporelles [Termes IGN] image Aqua-MODIS [Termes IGN] spectroradiométrie [Termes IGN] tenseur Résumé : (Auteur) Missing information, such as dead pixel values and cloud effects, is very common image quality degradation problems in remote sensing. Missing information can reduce the accuracy of the subsequent image processing, in applications such as classification, unmixing, and target detection, and even the quantitative retrieval process. The main aim of this paper is to study an adaptive weighted tensor completion (AWTC) method for the recovery of remote sensing images with missing data. Our idea is to collectively make use of the spatial, spectral, and temporal information to build a new weighted tensor low-rank regularization model for recovering the missing data. In the model, the weights are determined adaptively by considering the contribution of the spatial, spectral, and temporal information in each dimension. Experimental results based on both simulated and real data sets are presented to verify that the proposed method can recover missing data, and its performance is found to be better than the other tested methods. In the simulated experiments, the peak signal-to-noise ratio is improved by more than 3 dB, compared with the original tensor completion model. In the real data experiments, the proposed AWTC model can better recover the dead line problem in Aqua Moderate Resolution Imaging Spectroradiometer band 6 and the scan-line corrector-off problem in enhanced thematic mapper plus images, with the smallest spectral distortion. Numéro de notice : A2017-476 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2670021 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2670021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86401 [article]

Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5425 - 5439 Titre : Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation Type de document : Article/Communication Auteurs : Jie Li, Auteur ; Qiangqiang Yuan, Auteur ; Huanfeng Shen, Auteur ; Liangpei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 5425 - 5439 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage dirigé [Termes IGN] bruit (théorie du signal) [Termes IGN] données clairsemées [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] représentation parcimonieuse Résumé : (Auteur) Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve the quality of images for object detection, classification, and other subsequent applications. It has been reported that noise can be effectively removed using the sparsity in the nonnoise part of the image. With the appreciable redundancy and correlation in HSIs, the denoising performance can be greatly improved if this redundancy and correlation is utilized efficiently in the denoising process. Inspired by this observation, a noise reduction method based on joint spectral-spatial distributed sparse representation is proposed for HSIs, which exploits the intraband structure and the interband correlation in the process of joint sparse representation and joint dictionary learning. In joint spectral-spatial sparse coding, the interband correlation is exploited to capture the similar structure and maintain the spectral continuity. The intraband structure is utilized to adaptively code the spatial structure differences of the different bands. Furthermore, using a joint dictionary learning algorithm, we obtain a dictionary that simultaneously describes the content of the different bands. Experiments on both synthetic and real hyperspectral data show that the proposed method can obtain better results than the other classic methods. Numéro de notice : A2016-902 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2564639 En ligne : https://doi.org/10.1109/TGRS.2016.2564639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83095 [article]

Fusion of multi-scale DEMs using a regularized super-resolution method / Linwei Yue in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 12 (December 2015) . - pp 2095 - 2120 Titre : Fusion of multi-scale DEMs using a regularized super-resolution method Type de document : Article/Communication Auteurs : Linwei Yue, Auteur ; Huanfeng Shen, Auteur ; Qiangqiang Yuan, Auteur ; Liangpei Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 2095 - 2120 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] couverture (données géographiques) [Termes IGN] données multisources [Termes IGN] modèle numérique de terrain [Termes IGN] représentation multiple Résumé : (Auteur) The digital elevation model (DEM) is a significant digital representation of a terrain surface. Although a variety of DEM products are available, they often suffer from problems varying in spatial coverage, data resolution, and accuracy. However, the multi-source DEMs often contain supplementary information, which makes it possible to produce a higher-quality DEM through blending the multi-scale data. Inspired by super-resolution (SR) methods, we propose a regularized framework for the production of high-resolution (HR) DEM data with extended coverage. To deal with the registration error and the horizontal displacement among multi-scale measurements, robust data fidelity with weighted norm is employed to measure the conformance of the reconstructed HR data to the observed data. Furthermore, a slope-based Markov random field (MRF) regularization is used as the spatial regularization. The proposed method can simultaneously handle complex terrain features, noises, and data voids. Using the proposed method, we can reconstruct a seamless DEM data with the highest resolution among the input data, and an extensive spatial coverage. The experiments confirmed the effectiveness of the proposed method under different cases. Numéro de notice : A2015-620 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1063639 En ligne : https://doi.org/10.1080/13658816.2015.1063639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78089 [article]

Hyperspectral image denoising with a spatial–spectral view fusion strategy / Qiangqiang Yuan in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 5 tome 1 (May 2014) . - pp 2314 - 2325 Titre : Hyperspectral image denoising with a spatial–spectral view fusion strategy Type de document : Article/Communication Auteurs : Qiangqiang Yuan, Auteur ; Liangpei Zhang, Auteur ; Huanfeng Shen, Auteur Année de publication : 2014 Article en page(s) : pp 2314 - 2325 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] filtrage du bruit [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] image spatiale Résumé : (Auteur) In this paper, we propose a hyperspectral image denoising algorithm with a Spatial-spectral view fusion strategy. The idea is to denoise a noisy hyperspectral 3-D cube using the hyperspectral total variation algorithm, but applied to both the spatial and spectral views. A metric Q-weighted fusion algorithm is then adopted to merge the denoising results of the two views together, so that the denoising result is improved. A number of experiments illustrate that the proposed approach can produce a better denoising result than both the individual spatial and spectral view denoising results. Numéro de notice : A2014-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2259245 Date de publication en ligne : 15/07/2013 En ligne : https://doi.org/10.1109/TGRS.2013.2259245 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33162 [article]

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