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A spectral and spatial source separation of multispectral images / M.A. Loghmari in IEEE Transactions on geoscience and remote sensing, vol 44 n° 12 (December 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 12 (December 2006) . - pp 3659 - 3673 Titre : A spectral and spatial source separation of multispectral images Type de document : Article/Communication Auteurs : M.A. Loghmari, Auteur ; Mohamed Saber Naceur, Auteur ; Mohamed-Rached Boussema, Auteur Année de publication : 2006 Article en page(s) : pp 3659 - 3673 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification bayesienne [Termes IGN] données multisources [Termes IGN] hétérogénéité [Termes IGN] image multibande [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] séparabilité [Termes IGN] signature spectrale Résumé : (Auteur) This paper deals with the problem of blind source separation of remote sensing data based on a Bayesian estimation framework. We consider the case of multispectral images in which we have observed images of the same zone through different spectral bands. The land cover types existing in the scanned zone constitute the sources to separate. Associating each source to a specific significant theme remains the real challenge in the source-separation method applied to satellite images. In fact, multispectral images consist of multiple channels, each channel containing data acquired from different bands within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. This constitutes the first difficulty for sources identification. The second difficulty lies in the heterogeneity of most of the geological and vegetative ground surfaces. In this case, the geometrical projection of a single detector element at the Earth's surface, which is sometimes called the instantaneous field of view, is formed from a mixture of spectral signatures. In such circumstances, the needed information is either not available or not reliable. In this paper, the goal is to establish a new approach based on a two-level source separation (TLSS), which consists of a spectral separation along the different used bands and a spatial separation along neighboring pixels of each image band. The spectral separation has been used prior to the Bayesian approach, and it is based on a second-order statistics approach that exploits the correlation through different spectral bands of the multispectral sensor. The given images are represented according to independent axes that provide more effective representation of the information within the observation images. The spectral separation consists of identifying the sources without resorting to any a priori information, hence the term blind. The obtained source-separation represent the starting point for the Bayesian approach, which is known for its weakness in front of initial conditions. To identify a significant theme for each source, we have to spatially separate each image based on a Bayesian source-separation framework. The proposed approach has the added advantages of the blind source method as well as the Bayesian method. It should give segmented images related to each theme covering the scanned zone, which are the TLSS results of the observation images. Copyright IEEE Numéro de notice : A2006-559 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.882261 En ligne : https://doi.org/10.1109/TGRS.2006.882261 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28282 [article]

Exemplaires(1)

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065-06121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Estimation of the number of decomposition levels for a wavelet-based multiresolution multisensor image fusion / P. Pradhan in IEEE Transactions on geoscience and remote sensing, vol 44 n° 12 (December 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 12 (December 2006) . - pp 3674 - 3686 Titre : Estimation of the number of decomposition levels for a wavelet-based multiresolution multisensor image fusion Type de document : Article/Communication Auteurs : P. Pradhan, Auteur ; R.l. King, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 3674 - 3686 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] fusion d'images [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] limite de résolution géométrique [Termes IGN] qualité géométrique (image) [Termes IGN] qualité radiométrique (image) [Termes IGN] transformation en ondelettes Résumé : (Auteur) The wavelet-based scheme for the fusion of multispectral (MS) and panchromatic (PAN) imagery has become quite popular due to its ability to preserve the spectral fidelity of the MS imagery while improving its spatial quality. This is important if the resultant imagery is used for automatic classification. Wavelet-based fusion results depend on the number of decomposition levels applied in the wavelet transform. Too few decomposition levels result in poor spatial quality fused images. On the other hand, too many levels reduce the spectral similarity between the original MS and the pan-sharpened images. If the shift-invariant wavelet transform is applied, each excessive decomposition level results in a large computational penalty. Thus, the choice of the number of decomposition levels is significant. In this paper, PAN and MS image pairs with different resolution ratios were fused using the shift-invariant wavelet transform, and the optimal decomposition levels were determined for each resolution ratio. In general, it can be said that the fusion of images with larger resolution ratios requires a higher number of decomposition levels. This paper provides the practitioner an understanding of the tradeoffs associated with the computational demand and the spatial and spectral quality of the wavelet-based fusion algorithm as a function of the number of decomposition levels. Copyright IEEE Numéro de notice : A2006-560 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.881758 En ligne : https://doi.org/10.1109/TGRS.2006.881758 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28283 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-06121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Modelling and detection of geospatial objects using texture motifs / S. Bhagavathy in IEEE Transactions on geoscience and remote sensing, vol 44 n° 12 (December 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 12 (December 2006) . - pp 3706 - 3715 Titre : Modelling and detection of geospatial objects using texture motifs Type de document : Article/Communication Auteurs : S. Bhagavathy, Auteur Année de publication : 2006 Article en page(s) : pp 3706 - 3715 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse texturale [Termes IGN] apprentissage dirigé [Termes IGN] détection d'objet [Termes IGN] distribution spatiale [Termes IGN] image aérienne [Termes IGN] objet géographique [Termes IGN] texture d'image Résumé : (Auteur) We propose the use of texture motifs, or characteristic spatially recurrent patterns, for modeling and detecting geospatial objects. A method is proposed for learning a texture-motif model from object examples and detecting objects based on the learned model. The model is learned in a two-layered framework: the first learns the constituent "texture elements" of the motif and, the second, the spatial distribution of the elements. In the experimental session, we demonstrate the model training and selection methodology for objects given a set of training examples. The utility of such models for detecting the presence or absence of geospatial objects in large aerial image datasets comprising tens of thousands of image tiles is then emphasized. Copyright IEEE Numéro de notice : A2006-561 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.881741 En ligne : https://doi.org/10.1109/TGRS.2006.881741 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28284 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-06121	RAB	Revue	Centre de documentation	En réserve L003	Disponible