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Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images / Hao Cui in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2308 - 2323 Titre : Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images Type de document : Article/Communication Auteurs : Hao Cui, Auteur ; Peng Jia, Auteur ; Guo Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2308 - 2323 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse des correspondances [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] capteur à balayage [Termes descripteurs IGN] correction d'image [Termes descripteurs IGN] dégradation d'image [Termes descripteurs IGN] délignage [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] filtrage du rayonnement [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] itération [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] pollution acoustique Résumé : (auteur) Sensor instability, dark currents, and other factors often cause stripe noise corruption in hyperspectral remote sensing images and severely limit their application in practical purposes. Previous studies have proposed numerous destriping algorithms that have yielded impressive results. Although most destriping algorithms are based on the premise of additive noise, a few studies have focused directly on multiplicative stripe noise. This article fully analyzes the characteristics of the stripe noise of OHS-01 images and proposes a multiplicative stripe noise removal method. Specifically, stripe noise is tackled by performing radiometric normalization of different columns in the image. First, the relative gain coefficients of adjacent columns are separated based on prior knowledge. Second, the local relative intensity correspondence of the image columns are established by means of intensity propagation, intensity connection, and so on. Finally, the above-mentioned process is iterated in multiscale space, and the accumulated gain correction coefficient maps were used to correct the radiation of the original image. The results of extensive experiments on simulated and real remote sensing image data demonstrate that the proposed method can, in most cases, yield desirable results. In certain cases, the results are even better, visually, and quantitatively, than those obtained using classical algorithms. Moreover, the proposed method has high robustness and efficiency. Thus, it can conform to the requirements of engineering applications. Numéro de notice : A2020-194 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947599 date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947599 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94861 [article]

Reducing shadow effects on the co-registration of aerial image pairs / Matthew Plummer in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 177 - 186 Titre : Reducing shadow effects on the co-registration of aerial image pairs Type de document : Article/Communication Auteurs : Matthew Plummer, Auteur ; Douglas A. Stow, Auteur ; Emmanuel Storey, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 177 - 186 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] correction des ombres [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] effet d'ombre [Termes descripteurs IGN] enregistrement de données [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] masque [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] SIFT (algorithme) Résumé : (auteur) Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (SIFT) and Random Sample Consensus (RANSAC) Alignment (SARA). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows. Numéro de notice : A2020-147 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.4.177 date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.4.177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94776 [article]

Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1777 - 1789 Titre : Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints Type de document : Article/Communication Auteurs : Li Liu, Auteur ; Luping Xu, Auteur ; Houzhang Fang, Auteur Année de publication : 2020 Article en page(s) : pp 1777 - 1789 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse bivariée [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] interpolation polynomiale [Termes descripteurs IGN] itération [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] programmation par contraintes [Termes descripteurs IGN] texture d'image Résumé : (Auteur) Infrared (IR) images are often contaminated by obvious intensity bias and stripes, which severely affect the visual quality and subsequent applications. It is challenging to eliminate simultaneously the mixed nonuniformity noise without blurring the fine-image details in low-textured IR images. In this article, we present a new model for simultaneous intensity bias correction and destriping through introducing two sparsity constraints. One is that model fit on the intensity bias should be as accurate as possible. A bivariate polynomial model is built to characterize the global smoothness of the intensity bias. The other constraint is that the unidirectional variational sparse model can concisely represent the direction characteristic of stripe noise. A computationally efficient numerical algorithm based on split Bregman iteration is used to solve the complex optimization problem. The proposed method is fundamentally different from the existing denoising techniques and simultaneously estimates the sharp image, intensity bias, and stripe components. Significant improvement on image quality is achieved on both simulated and real studies. Both qualitative and quantitative comparisons with the state-of-the-art correction methods demonstrate its superiority. Numéro de notice : A2020-089 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2948601 date de publication en ligne : 18/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2948601 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94663 [article]

Automatic illumination-invariant image-to-geometry registration in outdoor environments / Christian Kehl in Photogrammetric record, vol 32 n° 158 (June - july 2017)  

Public [article]  inPhotogrammetric record > vol 32 n° 158 (June - july 2017) . - pp 93 - 118 Titre : Automatic illumination-invariant image-to-geometry registration in outdoor environments Type de document : Article/Communication Auteurs : Christian Kehl, Auteur ; Simon J. Buckley, Auteur ; Sophie Viseur, Auteur ; Robert L. Gawthorpe, Auteur ; John Howell, Auteur Année de publication : 2017 Article en page(s) : pp 93 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] algorithme d'appariement [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] géométrie de l'image [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] traitement d'image Résumé : (Auteur) Image-to-geometry registration is the basis of many applications for texturing and interpreting 3D surface models. Feature-based matching is an established, automatic approach which creates 2D–3D correspondences based on salient points and their radiometric neighbourhood. This paper presents an experimental approach for assessing the accuracy of several matching algorithms in challenging imaging environments that are subject to significant outdoor illumination variations. Furthermore, a collection of accuracy assessment metrics and quality heuristics emerge from the presented approach to guide a user during the examination of registration results. As a result of the experiments, two novel salient point descriptor matching combinations outperform the standard scale-invariant feature transform (SIFT) operator on the task of image-to-image and image-to-geometry registration under varying illumination conditions. Numéro de notice : A2017-361 Thématique : IMAGERIE Nature : Article En ligne : http://doi.org/10.1111/phor.12188 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85772 [article]

Hyperspectral and lidar intensity data fusion : A framework for the rigorous correction of illumination, anisotropic effects, and cross calibration / Maximilian Brell in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2799 - 2810 Titre : Hyperspectral and lidar intensity data fusion : A framework for the rigorous correction of illumination, anisotropic effects, and cross calibration Type de document : Article/Communication Auteurs : Maximilian Brell, Auteur ; Karl Segl, Auteur ; Luis Guanter, Auteur ; Bodo Bookhagen, Auteur Année de publication : 2017 Article en page(s) : pp 2799 - 2810 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] anisotropie [Termes descripteurs IGN] correction radiométrique [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] étalonnage croisé [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] réflectance spectrale Résumé : (Auteur) The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimensionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications. Numéro de notice : A2017-469 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2017.2654516 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86392 [article]

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)  

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Automated co-registration of satellite images through luminance transformation / Deniz Gerçek in Photogrammetric record, vol 31 n° 156 (December 2016 - February 2017)  

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An intensity recovery algorithm (IRA) for minimizing the edge effect of LIDAR data / Fabiane Bordin in European journal of remote sensing, vol 49 (2016)  

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Efficient terrestrial laser scan segmentation exploiting data structure / Hamid Mahmoudabadi in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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Satellite images analysis for shadow detection and building height estimation / Gregoris Liasis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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3D leaf water content mapping using terrestrial laser scanner backscatter intensity with radiometric correction / Xi Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)  

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Orthophotographie nocturne à haute résolution : la nuit, vue du ciel / Eva Frangiamone in Géomatique suisse, vol 112 n° 12 (décembre 2014)

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Orthophotographie nocturne à haute résolution / Anonyme in Géomatique expert, n° 100 (01/09/2014)

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Using thermal time and pixel purity for enhancing biophysical variable time series: An interproduct comparison / Grégory Duveiller in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)  

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Combination of overlap-driven adjustment and Phong model for LiDAR intensity correction / Q. Ding in ISPRS Journal of photogrammetry and remote sensing, vol 75 (January 2013)

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