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Modelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery / J. Pasher in Remote sensing of environment, vol 107 n° 3 (12 April 2007)
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Titre : Modelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery Type de document : Article/Communication Auteurs : J. Pasher, Auteur ; Dominique King, Auteur ; K. Lindsay, Auteur Année de publication : 2007 Article en page(s) : pp 471 - 483 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Aves
[Termes IGN] carte thématique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] habitat animal
[Termes IGN] image Ikonos
[Termes IGN] image Landsat
[Termes IGN] luminance lumineuse
[Termes IGN] Ontario (Canada)
[Termes IGN] photo-interprétation
[Termes IGN] précision de la classification
[Termes IGN] régression logistiqueRésumé : (Auteur) Modelling and mapping of hooded warbler (Wilsonia citrina) nesting habitat in forests of southern Ontario were conducted using Ikonos and Landsat data. The study began with an analysis of skyward hemispherical photography to determine canopy characteristics associated with nest sites. It showed that nest sites had significantly less overhead canopy cover and larger maximum gap size than in non-nest areas. These findings led to the hypothesis that brightness variability in high to moderate resolution remotely sensed imagery may be greater at nest sites than in non-nest areas due to larger shadows and greater shadow variability related to these gap characteristics. This was confirmed when, in addition to some spectral band brightness variables, several image texture and spectrally unmixed fraction (shadow, bare soil) variables were found to be significantly different for nest and non-nest sites in Ikonos and Landsat imagery. These significantly different variables were used in maximum likelihood classification (MLC) and logistic regression (LR) to produce maps of potential nesting habitat. Mapping was conducted with Ikonos and Landsat in a local area where most known nest sites occur, and regionally using Landsat data for almost all of the hooded warbler range in southern Ontario. For the local area mapping using Ikonos data, a posteriori probabilities for both the MLC and LR methods showed that about 62% of the nest sites set aside for validation had been classified with high probability (p > 0.70) in the nest class. MLC mapping accuracy was 70% for the validation nest sites and 87% of validation nest sites were within 10 m of classified nesting habitat, a distance approximately equivalent to expected positional error in the data. LR accuracy was slightly lower. Nest site MLC mapping accuracy in the local area using Landsat data was 87% but the map was much coarser due to the larger pixel size. Regional mapping with Landsat imagery produced lower classification accuracy due to high errors of commission for the habitat class. This resulted from a poor spatial distribution and low number of observations of nest sites throughout the region compared to the local area, while the non-nest site data distribution was too narrow, having been defined and assessed (using standard accepted methods) as areas with no ground shrubs. If either of these problems can be ameliorated, regional mapping accuracy may improve. Copyright Elsevier Numéro de notice : A2007-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.09.022 En ligne : https://doi.org/10.1016/j.rse.2006.09.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28502
in Remote sensing of environment > vol 107 n° 3 (12 April 2007) . - pp 471 - 483[article]Atmospheric correction algorithm for MERIS above case-2 waters / Th. Schroeder in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)
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Titre : Atmospheric correction algorithm for MERIS above case-2 waters Type de document : Article/Communication Auteurs : Th. Schroeder, Auteur ; I. Behnert, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 1469 - 1486 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] aérosol
[Termes IGN] classification par réseau neuronal
[Termes IGN] correction atmosphérique
[Termes IGN] image Envisat-MERIS
[Termes IGN] modèle de transfert radiatif
[Termes IGN] réflectanceRésumé : (Auteur) The development and validation of an atmospheric correction algorithm designed for the Medium Resolution Imaging Spectrometer (MERIS) with special emphasis on case-2 waters is described. The algorithm is based on inverse modelling of radiative transfer (RT) calculations using artificial neural network (ANN) techniques. The presented correction scheme is implemented as a direct inversion of spectral top-of-atmosphere (TOA) radiances into spectral remote sensing reflectances at the bottom-of-atmosphere (BOA), with additional output of the aerosol optical thickness (AOT) at four wavelengths for validation purposes. The inversion algorithm was applied to 13 MERIS Level 1b data tracks of 2002-2003, covering the optically complex waters of the North and Baltic Sea region. A validation of the retrieved AOTs was performed with coincident in situ automatic sun-sky scanning radiometer measurements of the Aerosol Robotic Network (AERONET) from Helgoland Island located in the German Bight. The accuracy of the derived reflectances was validated with concurrent ship-borne reflectance measurements of the SIMBADA hand-held field radiometer. Compared to the MERIS Level2 standard reflectance product generated by the processor versions 3.55, 4.06 and 6.3, the results of the proposed algorithm show a significant improvement in accuracy, especially in the blue part of the spectrum, where the MERIS Level 2 reflectances result in errors up to 122% compared to only 19% with the proposed algorithm. The overall mean errors within the spectral range of 412.5-708.75 nm are calculated to be 46.2% and 18.9% for the MERIS Level2 product and the presented algorithm, respectively. Copyright Taylor & Francis Numéro de notice : A2007-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600962574 En ligne : https://doi.org/10.1080/01431160600962574 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28539
in International Journal of Remote Sensing IJRS > vol 28 n°7-8 (April 2007) . - pp 1469 - 1486[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-07041 RAB Revue Centre de documentation En réserve L003 Disponible Improving land-cover classification using recognition threshold neural networks / M.J. Aitkenhead in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 4 (April 2007)
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Titre : Improving land-cover classification using recognition threshold neural networks Type de document : Article/Communication Auteurs : M.J. Aitkenhead, Auteur ; R. Dyer, Auteur Année de publication : 2007 Article en page(s) : pp 413 - 421 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] image Landsat
[Termes IGN] Philippines
[Termes IGN] seuillage d'image
[Termes IGN] surface cultivéeRésumé : (Auteur) The use of neural networks to classify land-cover from remote sensing imagery relies on the ability to determine a winner from the candidate land-cover types based on the imagery information available. In the case of a “winner- takes-all” scenario, this does not allow us a measure of how much the prediction of each pixel’s land-cover can be trusted. We present a three-stage method where only winning candidates which are given a clear lead over the other land-cover types are accepted, with a neighborhood relationship and the application of mixed pixels being used to provide full classification. This method allows us to place more faith in the resulting map than simply taking the winner, and results in a higher accuracy of classification. The method is applied to Landsat imagery of an area of the Philippines where natural, urban, and cultivated land-cover types exist. Copyright ASPRS Numéro de notice : A2007-143 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.4.413 En ligne : https://doi.org/10.14358/PERS.73.4.413 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28506
in Photogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 4 (April 2007) . - pp 413 - 421[article]Mapping land cover from detailed aerial photography data using textural and neural network analysis / R. Cots-Folch in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)
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Titre : Mapping land cover from detailed aerial photography data using textural and neural network analysis Type de document : Article/Communication Auteurs : R. Cots-Folch, Auteur ; M.J. Aitkenhead, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 1625 - 1642 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse texturale
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par réseau neuronal
[Termes IGN] image aérienne
[Termes IGN] paysage agricole
[Termes IGN] photographie panchromatique
[Termes IGN] utilisation du solRésumé : (Auteur) Automated mapping of land cover using black and white aerial photographs, as an alternative method to traditional photo-interpretation, requires using methods other than spectral analysis classification. To this end, textural measurements have been shown to be useful indicators of land cover. In this work, a neural network model is proposed and tested to map historical land use/land cover (LUC) from very detailed panchromatic aerial photographs (5m resolution) using textural measurements. The method is used to identify different land use and management types (e.g. traditional versus mechanized vineyard systems). These have been tested with known ground reference data. The results show the potential of the methodology to obtain automatic, historic, and very detailed cartography information from a complex landscape such as the mountainous and Mediterranean region to which it is applied here, and the advantages that this method has over traditional methods. Copyright Taylor & Francis Numéro de notice : A2007-177 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600887722 En ligne : https://doi.org/10.1080/01431160600887722 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28540
in International Journal of Remote Sensing IJRS > vol 28 n°7-8 (April 2007) . - pp 1625 - 1642[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-07041 RAB Revue Centre de documentation En réserve L003 Disponible An operational MISR pixel classifier using support vector machines / D. Mazzoni in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)
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Titre : An operational MISR pixel classifier using support vector machines Type de document : Article/Communication Auteurs : D. Mazzoni, Auteur ; M.J. Garay, Auteur ; R. Davies, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 149 - 158 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage automatique
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Terra-MISRRésumé : (Auteur) The Multi-angle Imaging SpectroRadiometer (MISR) data products now include a scene classification for each 1.1-km pixel that was developed using Support Vector Machines (SVMs), a cutting-edge machine learning technique for supervised classification. Using a combination of spectral, angular, and texture features, each pixel is classified as land, water, cloud, aerosol, or snow/ice, with the aerosol class further divided into smoke, dust, and other aerosols. The classifier was trained by MISR scientists who labeled hundreds of scenes using a custom interactive tool that showed them the results of the training in real time, making the process significantly faster. Preliminary validation shows that the accuracy of the classifier is approximately 81% globally at the 1.1-km pixel level. Applications of this classifier include global studies of cloud and aerosol distribution, as well as data mining applications such as searching for smoke plumes. This is one of the largest and most ambitious operational uses of machine learning techniques for a remote-sensing instrument, and the success of this system will hopefully lead to further use of this approach. Copyright Elsevier Numéro de notice : A2007-054 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.06.021 En ligne : https://doi.org/10.1016/j.rse.2006.06.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28419
in Remote sensing of environment > vol 107 n° 1-2 (15 March 2007) . - pp 149 - 158[article]A data-mining approach to associating MISR smoke plume heights with MODIS fire measurements / D. Mazzoni in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)
PermalinkSupport vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery / L. Su in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)
PermalinkSupport vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer / S. Durbha in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)
PermalinkComparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data / S. Lu in International Journal of Remote Sensing IJRS, vol 28 n°5-6 (March 2007)
PermalinkExtended Hausdorff distance for spatial objects in GIS / D. Min in International journal of geographical information science IJGIS, vol 21 n° 3-4 (march - april 2007)
PermalinkFeature extractions for small sample size classification problem / B.C. Kuo in IEEE Transactions on geoscience and remote sensing, vol 45 n° 3 (March 2007)
PermalinkMERIS-FR potential for land use-land cover mapping / S. Garcia-Gigorro in International Journal of Remote Sensing IJRS, vol 28 n°5-6 (March 2007)
PermalinkOil spill detection in Radarsat and Envisat SAR images / A.H. Solberg in IEEE Transactions on geoscience and remote sensing, vol 45 n° 3 (March 2007)
PermalinkTerrestrial and submerged aquatic vegetation mapping in Fire Island national seashore using high spatial resolution remote sensing data / Y. Wang in Marine geodesy, vol 30 n° 1-2 (March - June 2007)
PermalinkGeneration of geometrically and radiometrically terrain corrected SAR image products / A. Loew in Remote sensing of environment, vol 106 n° 3 (15/02/2007)
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