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Pixel unmixing in hyperspectral data by means of neural networks / Giorgio Licciardi in IEEE Transactions on geoscience and remote sensing, vol 49 n° 11 Tome 1 (November 2011)
[article]
Titre : Pixel unmixing in hyperspectral data by means of neural networks Type de document : Article/Communication Auteurs : Giorgio Licciardi, Auteur ; F. Del Frate, Auteur Année de publication : 2011 Article en page(s) : pp 4163 - 4172 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse en composantes principales
[Termes IGN] classification par réseau neuronal
[Termes IGN] image AHS
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] image PROBA-CHRIS
[Termes IGN] réduction géométrique
[Termes IGN] test de performanceRésumé : (Auteur) Neural networks (NNs) are recognized as very effective techniques when facing complex retrieval tasks in remote sensing. In this paper, the potential of NNs has been applied in solving the unmixing problem in hyperspectral data. In its complete form, the processing scheme uses an NN architecture consisting of two stages: the first stage reduces the dimension of the input vector, while the second stage performs the mapping from the reduced input vector to the abundance percentages. The dimensionality reduction is performed by the so-called autoassociative NNs, which yield a nonlinear principal component analysis of the data. The evaluation of the whole performance is carried out for different sets of experimental data. The first one is provided by the Airborne Hyperspectral Scanner. The second set consists of images from the Compact High-Resolution Imaging Spectrometer on board the Project for On-Board Autonomy satellite, and it includes multiangle and multitemporal acquisitions. The third set is represented by Airborne Visible/InfraRed Imaging Spectrometer measurements. A quantitative performance analysis has been carried out in terms of effectiveness in the dimensionality reduction phase and in terms of the accuracy in the final estimation. The results obtained, when compared with those produced by appropriate benchmark techniques, show the advantages of this approach. Numéro de notice : A2011-445 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2160950 Date de publication en ligne : 01/08/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2160950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31223
in IEEE Transactions on geoscience and remote sensing > vol 49 n° 11 Tome 1 (November 2011) . - pp 4163 - 4172[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2011111A RAB Revue Centre de documentation En réserve L003 Disponible 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]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2011111A RAB Revue Centre de documentation En réserve L003 Disponible 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]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2011091 RAB Revue Centre de documentation En réserve L003 Disponible In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system / Amr Abd-Elrahman in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)
[article]
Titre : In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system Type de document : Article/Communication Auteurs : Amr Abd-Elrahman, Auteur ; M. Croxton, Auteur ; Roshan Pande-Chhetri, Auteur ; et al., Auteur Année de publication : 2011 Article en page(s) : pp 463 - 472 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aquaculture
[Termes IGN] chlorophylle
[Termes IGN] cible cachée
[Termes IGN] étang
[Termes IGN] image hyperspectrale
[Termes IGN] qualité des eaux
[Termes IGN] turbidité des eauxRésumé : (Auteur) Knowledge of water quality parameters is integral to sustainability of freshwater aquaculture operations that raise ornamental fish. Our objective in this study is to evaluate the ability of a mobile, ground-based hyperspectral (HS) imaging sensor to determine chlorophyll-a (Chl-a) concentrations in working aquaculture ponds, which represent manipulated, shallow, nutrient-rich systems, and to determine the effect of using submerged reflectance targets on the accuracy of Chl-a estimation. We collected Chl-a measurements from aquaculture ponds ranging from 0.8 to 494 ug/L.. Chl-a measurements showed a strong correlation with two-band and three-band spectral indices computed from the HS image reflectance. Coefficient of determination (R2) values of 0.975 and 0.982 were obtained for the two- and three-band models, respectively, using spectra captured from the submerged target at 10 cm depth. Using spectra captured from water (no submerged targets), R2 values were slightly lower at 0.833 and 0.862 for two- and three-band models. Data from the submerged target at 30 cm depth had the lowest correlation with measured chlorophyll-a concentrations, potentially due to variations in water column properties and shadows cast by the platform. Modeling total Phosphorous (P) and Nitrogen (N) concentrations of the collected samples with the spectral indices sensitive to Chl-a concentrations showed a moderate level of correlation. Removing a model outlier (observation with maximum N and P concentrations) led to a significant increase in the models’ coefficient of determination (e.g. from 0.478 to 0.823 for the P model using three-band index values), which highlighted the possibility of using HS imagery to estimate N and P concentrations and the need for more research to model the interrelationships between Chl-a and nutrient concentrations in aquaculture water systems. Numéro de notice : A2011-298 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2011.02.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2011.02.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31077
in ISPRS Journal of photogrammetry and remote sensing > vol 66 n° 4 (July - August 2011) . - pp 463 - 472[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2011041 SL Revue Centre de documentation Revues en salle Disponible Integration of panoramic hyperspectral imaging with terrestrial lidar data / T. Kurz in Photogrammetric record, vol 26 n° 134 (June - August 2011)
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Titre : Integration of panoramic hyperspectral imaging with terrestrial lidar data Type de document : Article/Communication Auteurs : T. Kurz, Auteur ; S. Buckley, Auteur ; John Howell, Auteur Année de publication : 2011 Article en page(s) : pp 212 - 228 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] compensation par faisceaux
[Termes IGN] conflation
[Termes IGN] données lidar
[Termes IGN] géologie locale
[Termes IGN] image hyperspectrale
[Termes IGN] image panoramique
[Termes IGN] image terrestre
[Termes IGN] orientation externe
[Termes IGN] rayonnement infrarougeRésumé : (Auteur) In many close-range applications it is essential to obtain information about the geometry of the target surface as well as its chemical composition. In this study, close-range hyperspectral imaging was integrated with terrestrial laser scanning to provide mineral and chemical information for geological field studies. The spectral data was collected with the HySpex SWIR-320m sensor, which operates in the infrared spectrum between the wavelengths of 1·3 and 2·5 um. This sensor permits surfaces to be imaged with high spectral resolution, allowing detailed classification and analysis to be carried out. Photogrammetric processing of the hyperspectral imagery was achieved using an existing geometric model for rotating linear-array-based panoramic cameras. Bundle block adjustment of multiple images resulted in the registration of the spectral images in the lidar coordinate system, with a precision of around one image pixel. Although the image and control point network was not optimised for photogrammetric processing, it was possible to recover the exterior camera orientations, as well as additional camera calibration parameters. With the known image orientations, 3D lidar models could be textured with hyperspectral classifications, and the quality of the registration determined. The integration of the hyperspectral image products with the terrestrial lidar data enabled data interpretation and evaluation in a real-world coordinate system, and provided a reliable means of linking material and geometric information. Numéro de notice : A2011-241 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2011.00632.x Date de publication en ligne : 06/06/2011 En ligne : https://doi.org/10.1111/j.1477-9730.2011.00632.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31019
in Photogrammetric record > vol 26 n° 134 (June - August 2011) . - pp 212 - 228[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 106-2011021 RAB Revue Centre de documentation En réserve L003 Disponible Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)PermalinkMultiple Spectral–Spatial Classification Approach for Hyperspectral Data / Yuliya Tarabalka in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)Permalinkvol 48 n° 11 - November 2010 - Special issue on hyperspectral image and signal processing (Bulletin de IEEE Transactions on geoscience and remote sensing) / Geoscience and remote sensing societyPermalinkStatus and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment / B. Koch in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 6 (November - December 2010)PermalinkOpen source water quality analysis / A. Lo Tauro in GEO: Geoconnexion international, vol 9 n° 8 (september 2010)PermalinkMinimum dispersion constrained nonnegative matrix factorization to unmix hyperspectral data / A. Huck in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)PermalinkSuperresolution enhancement of hyperspectral CHRIS/Proba images with a thin-plate spline nonrigid transform model / J. Chan in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)PermalinkUse of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (Smile) in Hyperion Images / A. Dadon in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)PermalinkMerging hyperspectral and panchromatic image data: qualitative and quantitative analysis / M. Cetin in International Journal of Remote Sensing IJRS, vol 30 n° 7 (April 2009)PermalinkEvaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast / C. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)Permalink