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SVM-based unmixing-to-classification conversion for hyperspectral abundance quantification / F. Mianji in IEEE Transactions on geoscience and remote sensing, vol 49 n° 11 Tome 1 (November 2011)
[article]
Titre : SVM-based unmixing-to-classification conversion for hyperspectral abundance quantification Type de document : Article/Communication Auteurs : F. Mianji, Auteur ; Y. Zhang, Auteur Année de publication : 2011 Article en page(s) : pp 4318 - 4327 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse infrapixellaire
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] signature spectraleRésumé : (Auteur) Need for a priori knowledge of the components comprising each pixel in a scene has set the endmember determination, rather than the endmember abundance quantification, as the primary focus of many unmixing approaches. In the absence of the information about the pure signatures present in an image scene, which is often the case, the mean spectra of the pixel vectors, directly extracted from the scene, are usually used as the pure signatures' spectra. This approach which is mathematically optimized for unmixing problems with a priori known information ignores some statistical properties of the extracted samples and leads to a suboptimal solution for real situations. This paper proposes a novel learning-based unmixing-to-classification conversion model to treat the abundance quantification task as a classification problem. Support vector machine, as an efficient classifier, is used to realize this model. It exploits the statistical nature (endmember spectral variability) of the extracted endmember representatives from the hyperspectral scene, rather than solving the problem according to the ideal model in which only the mean spectra of each training sample set is used. Several experiments are carried out on simulated and real hyperspectral images. The obtained results validate the high performance of the proposed technique in abundance quantification which is a key subpixel information detection capability. Numéro de notice : A2011-446 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2166766 Date de publication en ligne : 06/10/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2166766 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31224
in IEEE Transactions on geoscience and remote sensing > vol 49 n° 11 Tome 1 (November 2011) . - pp 4318 - 4327[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2011111A RAB Revue Centre de documentation En réserve L003 Disponible Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data / S. Khorram in Geocarto international, vol 26 n° 6 (October 2011)
[article]
Titre : Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data Type de document : Article/Communication Auteurs : S. Khorram, Auteur ; H. Yuan, Auteur ; F. Van Der Wiele, Auteur Année de publication : 2011 Article en page(s) : pp 435 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multirésolution
[Termes IGN] carte de Kohonen
[Termes IGN] classification par réseau neuronal
[Termes IGN] données multicapteurs
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-TM
[Termes IGN] image SPOT
[Termes IGN] occupation du sol
[Termes IGN] Perceptron multicouche
[Termes IGN] précision de la classificationRésumé : (Auteur) Integrating multiple images with artificial neural networks (ANN) improves classification accuracy. ANN performance is sensitive to training datasets. Complexity and errors compound when merging multiple data, pointing to needs for new techniques. Kohonen's self-organizing mapping (KSOM) neural network was adapted as an automated data selector (ADS) to replace manual training data processes. The multilayer perceptron (MLP) network was then trained using automatically extracted datasets and used for classification. Two hypotheses were tested: ADS adapted from the KSOM network provides adequate and reliable training datasets, improving MLP classification performance; and fusion of Landsat Thematic Mapper (TM) and SPOT images using the modified ANN approach increases accuracy. ADS adapted from the KSOM network improved training data quality and increased classification accuracy and efficiency. Fusion of compatible multiple data can improve performance if appropriate training datasets are collected. This proved to be a viable classification scheme particularly where acquiring sufficient and reliable training datasets is difficult. Numéro de notice : A2011-402 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.600462 Date de publication en ligne : 10/08/2011 En ligne : https://doi.org/10.1080/10106049.2011.600462 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31181
in Geocarto international > vol 26 n° 6 (October 2011) . - pp 435 - 457[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2011061 RAB Revue Centre de documentation En réserve L003 Disponible Estimation of forest stand volume, tree density and biodiversity using Landsat ETM + Data, comparison of linear and regression tree analyses / Jahangir Mohammadi in Procedia Environmental Sciences, vol 7 (2011)
[article]
Titre : Estimation of forest stand volume, tree density and biodiversity using Landsat ETM + Data, comparison of linear and regression tree analyses Type de document : Article/Communication Auteurs : Jahangir Mohammadi, Auteur ; Shaban Shataee, Auteur ; Manoocher Babanezhad, Auteur Année de publication : 2011 Article en page(s) : pp 299 - 304 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] estimation statistique
[Termes IGN] image Landsat-ETM+
[Termes IGN] Iran
[Termes IGN] régression linéaire
[Termes IGN] régression multipleRésumé : (auteur) Estimation of forest attributes using remotely sensed data has being as a new potential for continuous management of natural resources. Simple statistical models such as linear regressions are most used approach that has been used by researchers. Applying other regression types in forest attribute estimations and their spatial modeling using decision tree analysis such as regression tree may be more usefulness compare to linear regression. In a case study in the Hyrcanian forests, northern of Iran, the capability of linear and regression tree analyses were compared to estimation of stand volume, tree density and tree diversity. Stepwise multiple regression and regression tree analyses were conducted to evaluate relationships between forest characteristics as dependent and ETM + bands and vegetation indices as independent variables. Performance assessment of models was examined using RMSE and Bias on the unused validation plots. The results of analysis showed that statistical models of stand volume, tree density, species richness and reciprocal of Simpson indices using tree regression analysis had higher adjusted R2 and CE compare to linear regression models. In addition, the performance results showed that RMSE of models using tree regression were 88.7 m3/ha, 157n/ha, 1.15 and 0.61 respectively for stand volume, tree density, species richness and Simpson index, Whereas, the RMSE of obtained models using linear regression were computed about 97m3/ha, 170n/ha, 1.51 and 1.15, respectively. Numéro de notice : A2011-630 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article DOI : 10.1016/j.proenv.2011.07.052 En ligne : https://doi.org/10.1016/j.proenv.2011.07.052 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102450
in Procedia Environmental Sciences > vol 7 (2011) . - pp 299 - 304[article]A model-driven approach to estimate atmospheric visibility with ordinary cameras / Raouf Babari in Atmospheric Environment, vol 45 n° 30 (September 2011)
[article]
Titre : A model-driven approach to estimate atmospheric visibility with ordinary cameras Type de document : Article/Communication Auteurs : Raouf Babari, Auteur ; Nicolas Hautière, Auteur ; Eric Dumont, Auteur ; Roland Brémond, Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2011 Article en page(s) : pp 5316 - 5324 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] caméra numérique
[Termes IGN] éclairement lumineux
[Termes IGN] qualité de l'air
[Termes IGN] scène
[Termes IGN] sécurité routière
[Termes IGN] visibilité (optique)Résumé : (Auteur) Atmospheric visibility is an important input for road and air transportation safety, as well as a good proxy to estimate the air quality. A model-driven approach is presented to monitor the meteorological visibility distance through use of ordinary outdoor cameras. Unlike in previous data-driven approaches, a physics-based model is proposed which describes the mapping function between the contrast distribution in the image and the atmospheric visibility. The model is non-linear, which allows encompassing a large spectrum of applications. The model assumes a continuous distribution of objects with respect to the distance in the scene and is estimated by a novel process. It is more robust to illumination variations by selecting the Lambertian surfaces in the scene. To evaluate the relevance of the approach, a publicly available database is used. When the model is fitted to short range data, the proposed method is shown to be effective and to improve on existing methods. In particular, it allows envisioning an easier deployment of these camera-based techniques on multiple observation sites. Numéro de notice : A2011-596 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.atmosenv.2011.06.053 Date de publication en ligne : 28/06/2011 En ligne : https://doi.org/10.1016/j.atmosenv.2011.06.053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91528
in Atmospheric Environment > vol 45 n° 30 (September 2011) . - pp 5316 - 5324[article]Simultaneous denoising and intrinsic order selection in hyperspectral imaging / M. Farzam in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)
[article]
Titre : Simultaneous denoising and intrinsic order selection in hyperspectral imaging Type de document : Article/Communication Auteurs : M. Farzam, Auteur ; S. Beheshti, Auteur Année de publication : 2011 Article en page(s) : pp 3423 - 3436 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bruit atmosphérique
[Termes IGN] classification automatique
[Termes IGN] estimation de précision
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] propagation d'erreur
[Termes IGN] rapport signal sur bruitRésumé : (Auteur) In this paper, we address the problem of order selection in noisy hyperspectral applications. In conventional unmixing methods, this problem has been divided into two separate processes of order selection and unmixing. Order selection methods generally use a denoising approach at the beginning stage. The data in this case pass through three stages: denoising, order selection, and unmixing. Each of these steps mainly aims to optimize a different criterion independently. In addition, any error created in the denoising process will be propagated not only to the order selection stage but also consequently to the unmixing results. Commonly used denoising methods such as eigenvalue-decomposition-based methods, e.g., singular-value-decomposition-based methods, provide a threshold value to separate the noise from the signal. These approaches are heavily sensitive to the threshold value and signal-to-noise ratio (SNR). Moreover, these methods tend to lose their efficiency rapidly for lower SNRs. Note that both the denoising step and the dimension estimation step aim to provide the optimum estimate of the same noiseless data. Consequently, adopting a simultaneous denoising and dimension estimation method with a goal to provide the optimum estimate of the desired noiseless data is rational. This process not only avoids possible error propagations from the denoising stage to the dimension estimation stage but also unifies the optimization criteria that were used in each of these steps. In this paper, a simultaneous denoising and dimension estimation method is introduced. The approach is based on minimizing the estimated mean square error. Minimization is done by comparing the estimated data in a range of subspaces dictated by a simultaneous process. Minimizing the error at once, the proposed method denoises the data and provides the optimum dimension simultaneously. Owing to the parallel processing of denoising and dimension estimation, the simulation results show the advantages of the proposed method over some of the state-of-the-art approaches and illustrate a substantial performance, particularly for cases with a lower SNR. Numéro de notice : A2011-363 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2119400 Date de publication en ligne : 29/04/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2119400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31142
in IEEE Transactions on geoscience and remote sensing > vol 49 n° 9 (September 2011) . - pp 3423 - 3436[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2011091 RAB Revue Centre de documentation En réserve L003 Disponible A multispectral and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery / X. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 7 (July 2011)PermalinkIntegration of panoramic hyperspectral imaging with terrestrial lidar data / T. Kurz in Photogrammetric record, vol 26 n° 134 (June - August 2011)PermalinkDétection de bateaux dans les images satellitaires optiques panchromatiques / N. Proia in Revue Française de Photogrammétrie et de Télédétection, n° 194 (Mai 2011)PermalinkImage fusion by spatially adaptive filtering using downscaling cokriging / E. Pardo-Iguzquiza in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 3 (May - June 2011)PermalinkA new pan-sharpening method using multiobjective particle swarm optimization and the shiftable contourlet transform / J. Saeedi in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 3 (May - June 2011)PermalinkConstruction of digital 3D highway model using stereo IKONOS satellite imagery / Ahmed Shaker in Geocarto international, vol 26 n° 1 (February 2011)PermalinkA genetic algorithm approach to moving threshold optimization for binary change detection / J. Im in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 2 (February 2011)PermalinkImpervious surface area extraction from IKONOS imagery using an object-based fuzzy method / Xuefei Hu in Geocarto international, vol 26 n° 1 (February 2011)Permalink2D change detection from satellite imagery: Performance analysis and impact of the spatial resolution of input images / Nicolas Champion (2011)PermalinkChange detection in a topographic building database using submetric satellite images / Arnaud Le Bris (2011)Permalink