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Striping noise detection and correction of remote sensing images / F. Tsai in IEEE Transactions on geoscience and remote sensing, vol 46 n° 12 (December 2008)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 46 n° 12 (December 2008) . - pp 4122 - 4131 Titre : Striping noise detection and correction of remote sensing images Type de document : Article/Communication Auteurs : F. Tsai, Auteur ; W. Chen, Auteur Année de publication : 2008 Article en page(s) : pp 4122 - 4131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] délignage [Termes IGN] filtrage du bruit [Termes IGN] filtre de Canny [Termes IGN] fonction spline d'interpolation [Termes IGN] image numérique [Termes IGN] niveau de gris (image) Résumé : (Auteur) This paper presents an image destriping system for correcting striping noise of remote-sensing images. The developed system identifies stripe positions based on edge-detection and line-tracing algorithms. Pixels not affected by striping are used as control points to construct cubic spline functions describing spatial gray level distributions of an image. Detected stripes are corrected by replacing the pixels with more reasonable gray values computed from constructed spline functions. Gray values of clean pixels not affected by stripes are not altered to preserve data genuineness. Several experimental results demonstrate that the developed system can correctly detect stripes in remote-sensing images and effectively repair them. Evaluations of the results based on an quantitative image quality index indicate that the image quality has been improved significantly after destriping. The destriped images are not only visually more plausible but also can provide better interpretability and are more suitable for computerized analysis. Copyright IEEE Numéro de notice : A2008-486 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2008.2000646 En ligne : https://doi.org/10.1109/TGRS.2008.2000646 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29555 [article]

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065-08121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Orthoimage creation of extremely high buildings / Guoqing Zhou in IEEE Transactions on geoscience and remote sensing, vol 46 n° 12 (December 2008)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 46 n° 12 (December 2008) . - pp 4132 - 4141 Titre : Orthoimage creation of extremely high buildings Type de document : Article/Communication Auteurs : Guoqing Zhou, Auteur ; W. Xie, Auteur ; Penggen Cheng, Auteur Année de publication : 2008 Article en page(s) : pp 4132 - 4141 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Orthophotographie, orthoimage [Termes IGN] contrainte géométrique [Termes IGN] Denver [Termes IGN] fonction orthogonale [Termes IGN] orthoimage intégrale [Termes IGN] orthorectification [Termes IGN] point d'appui [Termes IGN] superposition d'images [Termes IGN] tour (bâtiment) Résumé : (Auteur) This paper presents a method for creating orthoimage in the urban area of extremely high buildings. The proposed method in this paper is different from the traditional methods, which improved the accuracy by increasing the number and/or improved the geometric distribution of ground control points. This proposed method first established a mathematical model of constraint condition on the building edges, such as perpendicularity, and then the established constraint conditions are merged into the orthorectification model. A test field located in downtown of Denver, CO, has been used to evaluate our methods. The experiments of comparing the accuracy achieved by our method and other methods are conducted. The experimental results demonstrated that the proposed method can improve the accuracy of 2-5 ft for those buildings of over 100 m high and even 5-7 ft for those buildings over 100 m high in the margin of imagery. Copyright IEEE Numéro de notice : A2008-487 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2008.2002694 En ligne : https://doi.org/10.1109/TGRS.2008.2002694 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29556 [article]

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065-08121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An innovative method to classify remote-sensing images using Ant Colony Optimization / X. Liu in IEEE Transactions on geoscience and remote sensing, vol 46 n° 12 (December 2008)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 46 n° 12 (December 2008) . - pp 4198 - 4208 Titre : An innovative method to classify remote-sensing images using Ant Colony Optimization Type de document : Article/Communication Auteurs : X. Liu, Auteur ; X. Li, Auteur Année de publication : 2008 Article en page(s) : pp 4198 - 4208 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] discrétisation [Termes IGN] image TM (aérienne) [Termes IGN] optimisation par colonie de fourmis Résumé : (Auteur) This paper presents a new method to improve the classification performance for remote-sensing applications based on swarm intelligence. Traditional statistical classifiers have limitations in solving complex classification problems because of their strict assumptions. For example, data correlation between bands of remote-sensing imagery has caused problems in generating satisfactory classification using statistical methods. In this paper, ant colony optimization (ACO), based upon swarm intelligence, is used to improve the classification performance. Due to the positive feedback mechanism, ACO takes into account the correlation between attribute variables, thus avoiding issues related to band correlation. A discretization technique is incorporated in this ACO method so that classification rules can be induced from large data sets of remote-sensing images. Experiments of this ACO algorithm in the Guangzhou area reveal that it yields simpler rule sets and better accuracy than the See 5.0 decision tree method. Copyright IEEE Numéro de notice : A2008-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2008.2001754 En ligne : https://doi.org/10.1109/TGRS.2008.2001754 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29557 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-08121	RAB	Revue	Centre de documentation	En réserve L003	Disponible