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A physics-based unmixing method to estimate subpixel temperatures on mixed pixels / Manuel Cubero-Castan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
[article]
Titre : A physics-based unmixing method to estimate subpixel temperatures on mixed pixels Type de document : Article/Communication Auteurs : Manuel Cubero-Castan, Auteur ; Jocelyn Chanussot, Auteur ; Véronique Achard, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1894 - 1906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] emissivité
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image TASI
[Termes IGN] méthode des moindres carrés
[Termes IGN] rayonnement infrarouge thermique
[Termes IGN] température de luminanceRésumé : (Auteur) This paper presents a new algorithm for the analysis of linear spectral mixtures in the thermal infrared domain, with the goal to jointly estimate the abundance and the subpixel temperature in a mixed pixel, i.e., to estimate the relative proportion and the temperature of each material composing the mixed pixel. This novel approach is a two-step procedure. First, it estimates the emissivity and the temperature over pure pixels using the standard temperature and emissivity separation (TES) algorithm. Second, it estimates the abundance and the subpixel temperature using a new unmixing physics-based model, called Thermal Remote sensing Unmixing for Subpixel Temperature (TRUST). This model is based on an estimator of the subpixel temperature obtained by linearizing the black body law around the mean temperature of each material. The abundance is then retrieved by minimizing the reconstruction error with the estimation of the subpixel temperatures. The TRUST method is benchmarked on simulated scenes against the fully constrained least squares unmixing applied on the radiance and on the estimation of surface emissivity using the TES algorithm. The TRUST method shows better results on pure and mixed pixels composed of two materials. TRUST also shows promising results when applied on thermal hyperspectral data acquired with the Thermal Airborne Spectrographic Imager during the Detection in Urban scenario using Combined Airborne imaging Sensors campaign and estimates coherent localization of mixed-pixel areas. Numéro de notice : A2015-172 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2350771 Date de publication en ligne : 15/09/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2350771 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75890
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 1894 - 1906[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015041 RAB Revue Centre de documentation En réserve L003 Disponible An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
[article]
Titre : An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery Type de document : Article/Communication Auteurs : Yanfei Zhong, Auteur ; Yunyun Wu, Auteur ; Xiong Xu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1411 - 1426 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse infrapixellaire
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] classification du maximum a posteriori
[Termes IGN] classification pixellaire
[Termes IGN] image hyperspectrale
[Termes IGN] traitement automatique d'images
[Termes IGN] traitement de données localiséesRésumé : (Auteur) The subpixel mapping technique can specify the spatial distribution of different categories at the subpixel scale by converting the abundance map into a higher resolution image, based on the assumption of spatial dependence. Traditional subpixel mapping algorithms only utilize the low-resolution image obtained by the classification image downsampling and do not consider the spectral unmixing error, which is difficult to account for in real applications. In this paper, to improve the accuracy of the subpixel mapping, an adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery. In AMCDSM, to better simulate a real remote sensing scene, the low-resolution abundance images are obtained by the spectral unmixing method from the downsampled original image or real low-resolution images. The MAP model is extended by considering the spatial prior models (Laplacian, total variation (TV), and bilateral TV) to obtain the high-resolution subpixel distribution map. To avoid the setting of the regularization parameter, an adaptive parameter selection method is designed to acquire the optimal subpixel mapping results. In addition, in AMCDSM, to take into account the spectral unmixing error in real applications, a winner-take-all strategy is proposed to achieve a better subpixel mapping result. The proposed method was tested on simulated, synthetic, and real hyperspectral images, and the experimental results demonstrate that the AMCDSM algorithm outperforms the traditional subpixel mapping methods and provides a simple and efficient algorithm to regularize the ill-posed subpixel mapping problem. Numéro de notice : A2015-132 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2340734 Date de publication en ligne : 07/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2340734 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75796
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1411 - 1426[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Deblurring and sparse unmixing for hyperspectral images / Xi-Le Zhao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
[article]
Titre : Deblurring and sparse unmixing for hyperspectral images Type de document : Article/Communication Auteurs : Xi-Le Zhao, Auteur ; Fan Wang, Auteur ; Ting-Zhu Huang, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4045 - 4058 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] correction d'image
[Termes IGN] flou
[Termes IGN] image hyperspectraleRésumé : (Auteur) The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators for hyperspectral imaging whose point spread functions are generally system dependent and formed from axial optical aberrations in the acquisition system. An alternating direction method is developed to solve the resulting optimization problem efficiently. According to the structure of the TV regularization and sparse unmixing in the model, the convergence of the alternating direction method can be guaranteed. Experimental results are reported to demonstrate the effectiveness of the TV and sparsity model and the efficiency of the proposed numerical scheme, and the method is compared to the recent Sparse Unmixing via variable Splitting Augmented Lagrangian and TV method by Iordache et al. Numéro de notice : A2013-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227764 En ligne : https://doi.org/10.1109/TGRS.2012.2227764 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32513
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4045 - 4058[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion / N. Yokoya in IEEE Transactions on geoscience and remote sensing, vol 50 n° 2 (February 2012)
[article]
Titre : Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion Type de document : Article/Communication Auteurs : N. Yokoya, Auteur ; T. Yairi, Auteur ; A. Iwasaki, Auteur Année de publication : 2012 Article en page(s) : pp 528 - 537 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] matriceRésumé : (Auteur) Coupled nonnegative matrix factorization (CNMF) unmixing is proposed for the fusion of low-spatial-resolution hyperspectral and high-spatial-resolution multispectral data to produce fused data with high spatial and spectral resolutions. Both hyperspectral and multispectral data are alternately unmixed into end member and abundance matrices by the CNMF algorithm based on a linear spectral mixture model. Sensor observation models that relate the two data are built into the initialization matrix of each NMF unmixing procedure. This algorithm is physically straightforward and easy to implement owing to its simple update rules. Simulations with various image data sets demonstrate that the CNMF algorithm can produce high-quality fused data both in terms of spatial and spectral domains, which contributes to the accurate identification and classification of materials observed at a high spatial resolution. Numéro de notice : A2012-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2161320 Date de publication en ligne : 12/08/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2161320 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31495
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 2 (February 2012) . - pp 528 - 537[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012021 RAB Revue Centre de documentation En réserve L003 Disponible Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura, Japan) from Landsat TM data / Y. Oyama in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 1 (January - February 2009)
[article]
Titre : Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura, Japan) from Landsat TM data Type de document : Article/Communication Auteurs : Y. Oyama, Auteur ; B. Matsushita, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 73 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] analyse discriminante
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] décomposition d'image
[Termes IGN] image Landsat-TM
[Termes IGN] Japon
[Termes IGN] lac
[Termes IGN] matière organique
[Termes IGN] modélisation radiométrique de prise de vue
[Termes IGN] qualité des eaux
[Termes IGN] sédiment
[Termes IGN] turbidité des eauxRésumé : (Auteur) The remote sensing of Case 2 water has been far less successful than that of Case 1 water, due mainly to the complex interactions among optically active substances (e.g., phytoplankton, suspended sediments, colored dissolved organic matter, and water) in the former. To address this problem, we developed a spectral decomposition algorithm (SDA), based on a spectral linear mixture modeling approach. Through a tank experiment, we found that the SDA-based models were superior to conventional empirical models (e.g. using single band, band ratio, or arithmetic calculation of band) for accurate estimates of water quality parameters. In this paper, we develop a method for applying the SDA to Landsat-5 TM data on Lake Kasumigaura, a eutrophic lake in Japan characterized by high concentrations of suspended sediment, for mapping chlorophyll-a (Chl-a) and non-phytoplankton suspended sediment (NPSS) distributions. The results show that the SDA-based estimation model can be obtained by a tank experiment. Moreover, by combining this estimation model with satellite-SRSs (standard reflectance spectra : i.e., spectral endmembers) derived from biooptical modeling, we can directly apply the model to a satellite image. The same SDA-based estimation model for Chl-a concentration was applied to two Landsat-5 TM images, one acquired in April 1994 and the other in February 2006. The average Chl-a estimation error between the two was 9.9%, a result that indicates the potential robustness of the SDA-based estimation model. The average estimation error of NPSS concentration from the 2006 Landsat-5 TM image was 15.9%. The key point for successfully applying the SDA-based estimation model to satellite data is the method used to obtain a suitable satellite-SRS for each end-member. Numéro de notice : A2009-029 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2008.04.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2008.04.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29659
in ISPRS Journal of photogrammetry and remote sensing > vol 64 n° 1 (January - February 2009) . - pp 73 - 85[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-09011 SL Revue Centre de documentation Revues en salle Disponible Integration of Hyperion satellite data and a household social survey to caracterize the causes and consequences of reforestation patterns in the Northern Ecuadorian Amazon / S.J. Walsh in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 6 (June 2008)PermalinkEstimation of vegetation parameter for modelling soil erosion using linear spectral mixture analysis of Landsat ETM data / A.M. DE Asis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)PermalinkComparative analysis of urban reflectance and surface temperature / C. Small in Remote sensing of environment, vol 104 n° 2 (30 September 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)PermalinkIncorporating remote sensing information in modelling house values: a regression tree approach / D. Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 2 (February 2006)PermalinkSub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery / S. Lee in International Journal of Remote Sensing IJRS, vol 26 n° 22 (November 2005)PermalinkA land cover distribution composite image from coarse spatial resolution images using an unmixing method / T.M. Uenishi in International Journal of Remote Sensing IJRS, vol 26 n° 5 (March 2005)PermalinkEstimation of subpixel target size for remotely sensed imagery / C.I. Chang in IEEE Transactions on geoscience and remote sensing, vol 42 n° 6 (June 2004)PermalinkFusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data / Karl Segl in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 1-2 (June - December 2003)PermalinkLinear spectral random mixture analysis for hyperspectral imagery / C.I. Chang in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)Permalink