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Land-cover mapping in the Brazilian amazon using SPOT-4 Vegetation data and machine learning classification methods / João M.B. Carreiras in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 8 (August 2006)
[article]
Titre : Land-cover mapping in the Brazilian amazon using SPOT-4 Vegetation data and machine learning classification methods Type de document : Article/Communication Auteurs : João M.B. Carreiras, Auteur ; J.M.C. Pereira, Auteur ; Y.E. Shimabukuro, Auteur Année de publication : 2006 Article en page(s) : pp 897 - 910 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] carte d'occupation du sol
[Termes IGN] cartographie numérique
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] image SPOT-Végétation
[Termes IGN] Mato Grosso
[Termes IGN] occupation du solRésumé : (Auteur) The main objective of this study is to evaluate the feasibility of deriving a land-cover map of the state of Mato Grosso, Brazil, for the year 2000, using data from the 1 km SPOT-4 VEGETATION (VGT) sensor. For this purpose we used a VGT temporal series of 12 monthly composite images, which were further transformed to physical-meaningful fraction images of vegetation, soil, and shade. Classification of fraction images was implemented using several recent machine learning developments, namely, filtering input training data and probability bagging in a classification tree approach. A 10-fold cross validation accuracy assessment indicates that filtering and probability bagging are effective at increasing overall and class-specific accuracy. Overall accuracy and mean probability of class membership were 0.88 and 0.80, respectively. The map of probability of class membership indicates that the larger errors are associated with cerrado savonna and semi-deciduous forest. Copyright ASPRS Numéro de notice : A2006-313 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.72.8.897 En ligne : https://doi.org/10.14358/PERS.72.8.897 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28037
in Photogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 8 (August 2006) . - pp 897 - 910[article]On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance / F. Gao in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
[article]
Titre : On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance Type de document : Article/Communication Auteurs : F. Gao, Auteur ; J. Masek, Auteur ; M. Schwaller, Auteur ; D. Gramond, Auteur Année de publication : 2006 Article en page(s) : pp 2207 - 2218 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] accentuation d'image
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Terra-MODIS
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] réflectance du solRésumé : (Auteur) The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic "daily" surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes. Copyright IEEE Numéro de notice : A2006-395 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.872081 En ligne : https://doi.org/10.1109/TGRS.2006.872081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28119
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2207 - 2218[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06081 RAB Revue Centre de documentation En réserve L003 Disponible A patch-based image classification by integrating hyperspectral data with GIS / B. Zhang in International Journal of Remote Sensing IJRS, vol 27 n°15-16 (August 2006)
[article]
Titre : A patch-based image classification by integrating hyperspectral data with GIS Type de document : Article/Communication Auteurs : B. Zhang, Auteur ; Xiuping Jia, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 3337 - 3346 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] classification pixellaire
[Termes IGN] image hyperspectrale
[Termes IGN] image PHI
[Termes IGN] système d'information géographiqueRésumé : (Auteur) Hyperspectral remote sensing data provide detailed spectral information and are widely used for pixel-based image classification. However, without considering spatial correlation among neighbouring pixels, a generated thematic map may have a ‘salt-and-pepper’ appearance. With the development of the Geographic Information System (GIS), the spatial relationship between a pixel and its neighbours can be recorded readily and used together with remote sensing data. The objective of this study was to integrate hyperspectral data with the GIS for effective thematic mapping. To date, GIS data have been used mainly in field surveys or training field selection for remote sensing data interpretation. Here we propose a patch-classification based on integration of the GIS with remote sensing data. The classification results obtained by using this method can be easily saved in a vector format as used for GIS files. Computational cost is decreased compared with a pixel-by-pixel classification. The issue of how to identify pure or mixed patches is addressed and a three-level simple and effective checking method is developed. A case study is presented with a hyperspectral data set recorded by the Pushbroom Hyperspectral Imager (PHI) and related GIS data. Numéro de notice : A2006-337 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500409577 En ligne : https://doi.org/10.1080/01431160500409577 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28061
in International Journal of Remote Sensing IJRS > vol 27 n°15-16 (August 2006) . - pp 3337 - 3346[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-06081 RAB Revue Centre de documentation En réserve L003 Disponible A support vector method for anomaly detection in hyperspectral imagery / Amit Banerjee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)
[article]
Titre : A support vector method for anomaly detection in hyperspectral imagery Type de document : Article/Communication Auteurs : Amit Banerjee, Auteur ; Philippe Burlina, Auteur ; Chris Diehl, Auteur Année de publication : 2006 Article en page(s) : pp 2282 - 2291 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aide à la décision
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'erreur
[Termes IGN] détection de cible
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] test statistiqueRésumé : (Auteur) This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery. Copyright IEEE Numéro de notice : A2006-396 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.873019 En ligne : https://doi.org/10.1109/TGRS.2006.873019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28120
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 8 (August 2006) . - pp 2282 - 2291[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06081 RAB Revue Centre de documentation En réserve L003 Disponible Temporal influences on Landsat-5 thematic image in visible band / Y. Liu in International Journal of Remote Sensing IJRS, vol 27 n°15-16 (August 2006)
[article]
Titre : Temporal influences on Landsat-5 thematic image in visible band Type de document : Article/Communication Auteurs : Y. Liu, Auteur ; T. Hiyama, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 3183 - 3201 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] atmosphère terrestre
[Termes IGN] bande visible
[Termes IGN] changement climatique
[Termes IGN] effet atmosphérique
[Termes IGN] environnement
[Termes IGN] image Landsat-TM
[Termes IGN] image multitemporelle
[Termes IGN] invariantRésumé : (Auteur) The Landsat Thematic Mapper (TM) has provided fine spatial resolution data spanning two decades. These data are useful for long-term studies of environmental change. However, temporal factors such as sensor degradation, variation in Sun–target–satellite geometry, and variable atmospheric conditions can create inconsistencies in multi-temporal images and complicate data analysis. This study investigated temporal influences on satellite data. The methodology was developed based on a theoretically derived relationship between pixel values of pseudo-invariant features (PIFs) across time. The relationship was validated using multi-temporal Landsat-5 TM images, which showed that temporal factors contribute to PIF pixel values in both multiplicative and additive ways. For Landsat-5 TM level-0 data, temporal influences were simulated in terms of multiplicative and additive components. The results showed that atmospheric variation is the most influential factor, followed by variation in the Sun–target–satellite geometry and TM sensor degradation. Copyright Taylor & Francis Numéro de notice : A2006-336 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600647258 En ligne : https://doi.org/10.1080/01431160600647258 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28060
in International Journal of Remote Sensing IJRS > vol 27 n°15-16 (August 2006) . - pp 3183 - 3201[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-06081 RAB Revue Centre de documentation En réserve L003 Disponible Resolution dependent errors in remote sensing of cultivated areas / M. Ozdogan in Remote sensing of environment, vol 103 n° 2 (30/07/2006)PermalinkFuzzy classification: a case study using Landsat TM images in Iran / A.M. Lak in GIM international, vol 20 n° 7 (July 2006)PermalinkSome issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkApport de la classification combinée supervisée et non supervisée d'une image Landsat ETM+ à la cartographie géologique de la boutonnière de Kerdous, anti-atlas, Maroc / M. Hakdaoui in Photo interprétation, vol 42 n° 2 (Juin 2006)PermalinkArtificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery / H. Mills in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)PermalinkCo-registration and inter-sensor comparison of MODIS and LANDSAT 7 ETM+ data aimed at NDVI calculation / P. Boccardo in Revue Française de Photogrammétrie et de Télédétection, n° 182 (Juin 2006)PermalinkLocalized soft classification for super-resolution mapping of the shoreline / Aidy M. Muslim in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)PermalinkA new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter / M. Choi in IEEE Transactions on geoscience and remote sensing, vol 44 n° 6 (June 2006)PermalinkSubpixel analysis of Landsat ETM/sup +/ using self-organizing map (SOM) neural networks for urban land cover characterization / S. Lee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 6 (June 2006)PermalinkHigh-resolution image fusion: methods to preserve spectral and spatial resolution / A. Svab in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 5 (May 2006)Permalink