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A study of scattering from an object below a rough surface / Joel T. Johnson in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 1 (January 2004) . - pp 59 - 66 Titre : A study of scattering from an object below a rough surface Type de document : Article/Communication Auteurs : Joel T. Johnson, Auteur ; R.J. Burkholder, Auteur Année de publication : 2004 Article en page(s) : pp 59 - 66 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] diffusomètre [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle de diffusion du rayonnement [Termes IGN] radar [Termes IGN] rayonnement électromagnétique Résumé : (Auteur) A numerical model is applied in a Monte Carlo study at scattering from a three dimensional penetrable object below a lossy dielectric rough interface. Both time and frequency domain results are investigated to illustrate the relative importance of coherent and incoherent scattering effects in the sample problem considered. Results show that introducing a reduced transmission coefficient is reasonable for object coherent scattering predictions in this example, and that incoherent object/surface interaction effects approximately follow a simple scaling behavior as surface roughness is increased. Numéro de notice : A2004-043 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/APS.2003.1219875 En ligne : https://doi.org/10.1109/APS.2003.1219875 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26571 [article]

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Bayesian-based subpixel brightness temperature estimation from multichannel infrared GOES radiometer data / S. Cain in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 1 (January 2004) . - pp 188 - 201 Titre : Bayesian-based subpixel brightness temperature estimation from multichannel infrared GOES radiometer data Type de document : Article/Communication Auteurs : S. Cain, Auteur Année de publication : 2004 Article en page(s) : pp 188 - 201 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification bayesienne [Termes IGN] cohérence des données [Termes IGN] image GOES [Termes IGN] image multibande [Termes IGN] image NOAA-AVHRR [Termes IGN] image optique [Termes IGN] luminance lumineuse [Termes IGN] précision infrapixellaire [Termes IGN] rayonnement infrarouge [Termes IGN] température de luminance Résumé : (Auteur) In this paper, a new image reconstruction scheme is devised for estimating a high-resolution temperature map of the top of the earth's atmosphere using the Geostationary Operational Environmental Satellite (GOES) imager infrared channels 4 and 5. By simultaneously interpolating the image while estimating temperature, the proposed algorithm achieves a more accurate estimate of the subpixel temperatures than could be obtained by performing these operations independently of one another. The proposed algorithm differs from other Bayesian-based image interpolation schemes in that it estimates brightness temperature as opposed to image intensity and incorporates a detailed optical model of the GOES multichannel imaging system. In order to test the effectiveness of the proposed technique, high-resolution estimates of cloudtop temperatures using GOES channels 4 and 5 are compared to temperature estimates obtained from the Advanced Very High Resolution Radiometer (AVHRR). This test is achieved by examining sets of infrared data taken simultaneously by the GOES and AVHRR systems over the same geographic area. The AVHRR system collects longwave infrared data with a spatial resolution of 1km, which is higher than the 4km spatial resolution the GOES system achieves. In some cases, the estimated temperature differences between these systems are as high as 11.5 K. It is shown in this paper that the proposed algorithm improves the consistency between the cloudtop temperatures estimated with the GOES and AVHRR systems by allowing the GOES system to achieve substantially higher spatial resolution. Numéro de notice : A2004-044 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.815397 En ligne : https://ieeexplore.ieee.org/document/1262596 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26572 [article]

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Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery / D.A. Clausi in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 1 (January 2004) . - pp 215 - 228 Titre : Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery Type de document : Article/Communication Auteurs : D.A. Clausi, Auteur ; B. Yue, Auteur Année de publication : 2004 Article en page(s) : pp 215 - 228 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse texturale [Termes IGN] champ aléatoire de Markov [Termes IGN] glace [Termes IGN] image Radarsat [Termes IGN] niveau de gris (image) [Termes IGN] segmentation d'image [Termes IGN] télédétection spatiale [Termes IGN] texture d'image Résumé : (Auteur) This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive in texture boundary confusion than MRFs given their respective segmentation procedures. Numéro de notice : A2004-045 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817218 En ligne : https://doi.org/10.1109/TGRS.2003.817218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26573 [article]

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A cost-effective semisupervised classifier approach with kernels / M. Murat Dundar in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 1 (January 2004) . - pp 264 - 270 Titre : A cost-effective semisupervised classifier approach with kernels Type de document : Article/Communication Auteurs : M. Murat Dundar, Auteur ; D.L. Landgrebe, Auteur Année de publication : 2004 Article en page(s) : pp 264 - 270 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classificateur [Termes IGN] classification semi-dirigée [Termes IGN] distribution de Fisher [Termes IGN] échantillon [Termes IGN] itération [Termes IGN] méthode fondée sur le noyau Résumé : (Auteur) In this paper, we propose a cost-effective iterative semisupervised classifier based on a kernel concept. The proposed technique incorporates unlabeled data into the design of a binary classifier by introducing and optimizing a cost function in a feature space that maximizes the Rayleigh coefficient while minimizing the total cost associated with misclassified labeled samples. The cost assigned to misclassified labeled samples accounts for the number of misclassified labeled samples as well as the amount by which they are on the wrong side of the boundary, and this counterbalances any potential adverse effect of unlabeled data on the classifier performance. Several experiments performed with remotely sensed data demonstrate that using the proposed semisupervised classifier shows considerable improvements over the supervised-only counterpart. Numéro de notice : A2004-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817815 En ligne : https://doi.org/10.1109/TGRS.2003.817815 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26574 [article]

Exemplaires(1)

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065-04011	RAB	Revue	Centre de documentation	En réserve L003	Disponible