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Faraday rotation correction for the SMAP radiometer / David M. Le Vine in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 4 (April 2016) . - pp 2070 - 2081 Titre : Faraday rotation correction for the SMAP radiometer Type de document : Article/Communication Auteurs : David M. Le Vine, Auteur ; Saji Abraham, Auteur ; Jinzheng Peng, Auteur Année de publication : 2016 Article en page(s) : pp 2070 - 2081 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] évaluation des données [Termes IGN] humidité du sol [Termes IGN] mission SMAP [Termes IGN] radiomètre [Termes IGN] rotation de Faraday [Termes IGN] Soil Moisture Active Passive [Termes IGN] surface de l'eau [Termes IGN] surface du sol Résumé : (Auteur) Faraday rotation is an important issue for remote sensing of parameters such as soil moisture and ocean salinity, which are best done at low microwave frequency (e.g., L-band). Modern instruments such as the radiometer on the Soil Moisture and Ocean Salinity (SMOS) satellite and the Aquarius radiometers include polarimetric radiometer channels specifically to implement a correction for Faraday rotation. This works well over ocean, but it is known that over inhomogeneous scenes, such as a land/water mixture, significant errors can occur. This is a particularly important issue for the newest L-band sensor in space, the radiometer on the Soil Moisture Active Passive (SMAP) satellite, where the goal is remote sensing over land (soil moisture) and where the conical scan induces rapid variation in Faraday rotation. Analysis is presented here of the issues associated with retrieving Faraday rotation using the SMAP geometry and antenna pattern. It is shown that, in addition to scenes with a mixture of land and water, scenes with significant vegetation canopy are also associated with large errors in the retrieved Faraday rotation. Examples from the SMAP radiometer support the analysis. Numéro de notice : A2016-839 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2495168 En ligne : https://doi.org/10.1109/TGRS.2015.2495168 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82883 [article]

An unbiased algorithm for detection of curvilinear structures in urban remote sensing images / Jinzheng Peng in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°23-24 (December 2007) . - pp 5377 - 5395 Titre : An unbiased algorithm for detection of curvilinear structures in urban remote sensing images Type de document : Article/Communication Auteurs : Jinzheng Peng, Auteur ; Ya-Qiu Jin, Auteur Année de publication : 2007 Article en page(s) : pp 5377 - 5395 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme de Gauss [Termes IGN] courbe [Termes IGN] extraction automatique [Termes IGN] extraction du réseau routier [Termes IGN] image aérienne [Termes IGN] objet géographique linéaire Résumé : (Auteur) Based on the Gaussian scale-space, a Gaussian comparison function is presented for extracting the linearly road features in aerial remote sensing image. Combining the geometric and radiometric features, the curvilinear structures of the roads are extracted based on locally oriented energy in continuous scale-space. Curvilinear features of roads are verified, grouped and extracted by using both topologic and geometric methods. This algorithm is applicable to extracting the road features in different scale such as rural roads or urban highways, and significantly reduces the computation complexity of line tracing. Some discussions on the zero drift of the Gaussian comparison function are also presented. Copyright Taylor & Francis Numéro de notice : A2007-537 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160601075574 En ligne : https://doi.org/10.1080/01431160601075574 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28900 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07131	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Using maximum likelihood (ML) and maximum a prior probability (MAP) in iterative self-organizing data (Isodata) / Hassan A. Karimi in Geocarto international, vol 19 n° 1 (March - May 2004)  

Public [article]  inGeocarto international > vol 19 n° 1 (March - May 2004) . - pp 29 - 36 Titre : Using maximum likelihood (ML) and maximum a prior probability (MAP) in iterative self-organizing data (Isodata) Type de document : Article/Communication Auteurs : Hassan A. Karimi, Auteur ; Jinzheng Peng, Auteur Année de publication : 2004 Article en page(s) : pp 29 - 36 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carte de Kohonen [Termes IGN] classification non dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] image à très haute résolution [Termes IGN] image satellite [Termes IGN] itération Résumé : (Auteur) With the availability of high-resolution satellite imagery featuring as high as 1 meter resolution in the panchromatic mode and 3-meter resolution in the multispectral mode, there is an interest by many new commercial and public service applications such as cellular telephones coverage area design, urban/land cover planning, and real estate marketing to extract features from images automatically. To that end, the demand for unsupervised classification techniques is growing. In this paper, the Maximum Likelihood (ML) and Maximum A prior Probability (MAP) algorithms are used as decision rules to find boundaries of classes computed by the Iterative Self-Organizing Data (ISOADATA) algorithm. Different satellite images with different resolutions were used to experiment with these algorithms. The results of comparing and analyzing the algorithms revealed that MAP-ISODATA performed better than ML-ISODATA even when the same initial matrix was used. It was shown that there was no significant différence between ML-ISODATA and MAP-ISODATA in terms of accuracy. It was also realized that better results could be obtained if homogenous initialization strategies were used. Numéro de notice : A2004-232 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040408542296 Date de publication en ligne : 02/01/2008 En ligne : https://doi.org/10.1080/10106040408542296 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26759 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-04011	RAB	Revue	Centre de documentation	En réserve L003	Disponible