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Auteur Pai-Hui Hsu |
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A robust digital watermarking algorithm for copyright protection of aerial photogrammetric images / Pai-Hui Hsu in Photogrammetric record, vol 31 n° 153 (March - May 2016)
[article]
Titre : A robust digital watermarking algorithm for copyright protection of aerial photogrammetric images Type de document : Article/Communication Auteurs : Pai-Hui Hsu, Auteur ; Chih-Cheng Chen, Auteur Année de publication : 2016 Article en page(s) : pp 51 - 70 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] droit d'auteur
[Termes IGN] image aérienne
[Termes IGN] méthode robuste
[Termes IGN] tatouage numérique
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Current research on digital watermarking for the copyright protection of digital multimedia data has led to fairly advanced techniques with fruitful results. However, there remains a lack of research on digital watermarking for geospatial data, which is very costly to produce but is of great importance and with wide application. In this study an analysis and discussion of digital watermarking is carried out for digital aerial photogrammetric images. Focusing on the requirements for the main applications of such images, a feature-based digital watermarking algorithm is proposed. Testing and analysis of the robustness of the watermark is performed to achieve the goal of copyright protection, even after image processing and geometric transformation have been undertaken on the watermarked image. Furthermore, the image quality is (almost) preserved to avoid detrimental effects on subsequent applications. The experimental results prove that the proposed watermarking method has a certain degree of robustness and can resist most types of image-processing and geometric attacks, while maintaining the data quality of the aerial images. Numéro de notice : A2016-161 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12134 Date de publication en ligne : 29/02/2016 En ligne : https://doi.org/10.1111/phor.12134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80461
in Photogrammetric record > vol 31 n° 153 (March - May 2016) . - pp 51 - 70[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2016011 RAB Revue Centre de documentation En réserve L003 Disponible Feature extraction of hyperspectral images using wavelet and matching pursuit / Pai-Hui Hsu in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 2 (June 2007)
[article]
Titre : Feature extraction of hyperspectral images using wavelet and matching pursuit Type de document : Article/Communication Auteurs : Pai-Hui Hsu, Auteur Année de publication : 2007 Article en page(s) : pp 78 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Since hyperspectral images contain rich and fine spectral information, an improvement of land use/cover classification accuracy is highly expected from the utilization of such images. However, the traditional statistics-based classification methods which have been successfully applied to multispectral data in the past are not as effective as to hyperspectral data. One major reason is that the number of spectral bands is too large relative to the number of training samples. This problem is caused by curse of dimensionality, which refers to the fact that the sample size required for training a specific classifier grows exponentially with the number of spectral bands. A simple but sometimes very effective way to overcome this problem is to reduce the dimensionality of hyperspectral images. This can be done by feature extraction that a small number of salient features are extracted from the hyperspectral data when confronted with a limited size of training samples. In this paper, a new feature extraction method based on the matching pursuit (MP) is proposed to extract useful features for the classification of hyperspectral images. The matching pursuit algorithm uses a greedy strategy to find an adaptive and optimal representation of the hyperspectral data iteratively from a highly redundant wavelet packets dictionary. An AVIRIS data set was tested to illustrate the classification performance after matching pursuit method was applied. In addition, some existing feature extraction methods based on the wavelet transform are also compared with the matching pursuit method in terms of the classification accuracies. The experiment results showed that the wavelet and matching pursuit method exactly provide an effective tool for feature extraction. The classification problem caused by curse of dimensionality can be avoided by matching pursuit and wavelet-based dimensionality reduction. Copyright ISPRS Numéro de notice : A2007-256 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2006.12.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2006.12.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28619
in ISPRS Journal of photogrammetry and remote sensing > vol 62 n° 2 (June 2007) . - pp 78 - 92[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-07041 SL Revue Centre de documentation Revues en salle Disponible