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Quality criteria benchmark for hyperspectral imagery / E. Christophe in IEEE Transactions on geoscience and remote sensing, vol 43 n° 9 (September 2005)
[article]
Titre : Quality criteria benchmark for hyperspectral imagery Type de document : Article/Communication Auteurs : E. Christophe, Auteur ; D. Leger, Auteur ; Corinne Mailhes, Auteur Année de publication : 2005 Article en page(s) : pp 2103 - 2114 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] classification
[Termes IGN] compression de données
[Termes IGN] dégradation d'image
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] qualité des données
[Termes IGN] test de performanceRésumé : (Auteur) Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Efficient compression techniques are required, and lossy compression, specifically, will have a role to play, provided its impact on remote sensing applications remains insignificant. To assess the data quality, suitable distortion measures relevant to end-user applications are required. Quality criteria are also of a major interest for the conception and development of new sensors to define their requirements and specifications. This paper proposes a method to evaluate quality criteria in the context of hyperspectral images. The purpose is to provide quality criteria relevant to the impact of degradations on several classification applications. Different quality criteria are considered. Some are traditionnally used in image and video coding and are adapted here to hyperspectral images. Others are specific to hyperspectral data. We also propose the adaptation of two advanced criteria in the presence of different simulated degradations on AVIRIS hyperspectral images. Finally, live criteria are selected to give an accurate representation of the nature and the level of the degradation affecting hyperspectral data. Numéro de notice : A2005-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2005.853931 En ligne : https://doi.org/10.1109/TGRS.2005.853931 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27524
in IEEE Transactions on geoscience and remote sensing > vol 43 n° 9 (September 2005) . - pp 2103 - 2114[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-05091 RAB Revue Centre de documentation En réserve L003 Disponible An automatic nonlinear correlation approach for processing of hyperspectral images / R.N. Ingram in International Journal of Remote Sensing IJRS, vol 25 n° 22 (November 2004)
[article]
Titre : An automatic nonlinear correlation approach for processing of hyperspectral images Type de document : Article/Communication Auteurs : R.N. Ingram, Auteur ; A.S. Lewis, Auteur ; R.L. Tutwiler, Auteur Année de publication : 2004 Article en page(s) : pp 4981 - 4998 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] équation non linéaire
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robuste
[Termes IGN] traitement parallèleRésumé : (Auteur) Hyperspectral imaging technology demands sophisticated processing techniques that offer precise characterizations of complex spectral signatures. A nonlinear correlator structure is implemented for interference mitigation and object recognition. A key asset is the correlator's applicability to both the spatial (two-dimensional) and spectral (one-dimensional) domains, thus ideal for hyperspectral processing. The process consists of a standard convolution summed with a nonlinear adaptive term. The premise is the same in each case but the mathematical implementation is different. By performing the correlation calculations in the frequency domain, the processing algorithm is efficient, robust, and well suited for implementation on a parallel processing computational architecture. The nonlinear correlator depends on two parameters and an algorithm to determine these parameters based only on the input image (two-dimensional) or spectral signature (one-dimensional) is presented. Based on the results with the selected spatial and spectral templates, a target is identified and the spatial coordinates as well as the spectral signature are input to the final fusion stage, which analyses both spectral and spatial signatures for a correct target identification. Several examples are given and insights to template (mask) selection are provided. Numéro de notice : A2004-489 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160410001680455 En ligne : https://doi.org/10.1080/01431160410001680455 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27007
in International Journal of Remote Sensing IJRS > vol 25 n° 22 (November 2004) . - pp 4981 - 4998[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-04201 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Classification of hyperspectral remote sensing images with support vector machines / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 42 n° 8 (August 2004)
[article]
Titre : Classification of hyperspectral remote sensing images with support vector machines Type de document : Article/Communication Auteurs : F. Melgani, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2004 Article en page(s) : pp 1778 - 1790 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification barycentrique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectraleRésumé : (Auteur) This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines (SVMs). First, we propose a theoretical discussion and experimental analysis aimed at understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces. Then, we assess the effectiveness of SVMs with respect to conventional feature-reduction-based approaches and their performances in hypersubspaces of various dimensionalities. To sustain such an analysis, the performances of SVMs are compared with those of two other nonparametric classifiers (i.e., radial basis function neural networks and the K-nearest neighbor classifier). Finally, we study the potentially critical issue of applying binary SVMs to multiclass problems in hyperspectral data. In particular, four different multiclass strategies are analyzed and compared: the one-against-all, the one-against-one, and two hierarchical tree-based strategies. Different performance indicators have been used to support our experimental studies in a detailed and accurate way, i.e., the classification accuracy, the computational time, the stability to parameter setting, and the complexity of the multiclass architecture. The results obtained on a real Airborne Visible/Infrared Imaging Spectroradiometer hyperspectral dataset allow to conclude that, whatever the multiclass strategy adopted, SVMs are a valid and effective alternative to conventional pattern recognition approaches (feature-reduction procedures combined with a classification method) for the classification of hyperspectral remote sensing data. Numéro de notice : A2004-389 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.831865 En ligne : https://doi.org/10.1109/TGRS.2004.831865 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26916
in IEEE Transactions on geoscience and remote sensing > vol 42 n° 8 (August 2004) . - pp 1778 - 1790[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-04081 RAB Revue Centre de documentation En réserve L003 Disponible Linear mixture analysis-based compression for hyperspectral image analysis / Q. Du in IEEE Transactions on geoscience and remote sensing, vol 42 n° 4 (April 2004)
[article]
Titre : Linear mixture analysis-based compression for hyperspectral image analysis Type de document : Article/Communication Auteurs : Q. Du, Auteur ; C.I. Chang, Auteur Année de publication : 2004 Article en page(s) : pp 875 - 891 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image numérique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] compression de données
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectraleRésumé : (Auteur) Due to significantly improved spectral resolution produced by hyperspectral sensors, the hand-to-hand correlation is generally very high and can be removed without loss of crucial information. Data compression is an effective means to eliminate such redundancy resulting from high interband correlation. In hyperspectral imagery, various information comes from different signal sources, which include man-made targets, natural backgrounds, unknown clutters, interferers, unidentified anomalies, etc. In many applications, whether or not a compression technique is effective is measured by the degree of information loss rather than information recovery. For example, compression of noise or interferers is highly desirable to image analysis and interpretation. In this paper, we present an unsupervised fully constrained least squares (UFCLS) linear spectral mixture analysis (LSMA)-based compression technique for hyperspectral target detection and classification. Unlike most compression techniques, which deal directly with grayscale images, the proposed compression approach generates and encodes the fractional abundance images of targets of interest present in an image scene to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in these fractional abundance images, the loss of information may have little impact on image analysis. On some occasions, it even improves performance analysis. Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data are used for experiments to evaluate our proposed LSMA-based compression technique used for applications in hyperspectral detection and image classification. The classification results using the original data and the UFCLS-decompressed data are shown to be very close with no visible difference. But a compression ratio for the HYDICE data with water bands removed can achieve as high as 138: 1 with peak SNR (PSNR) 33 dB, while a compression ratio of the AVIRIS scene also with water bands removed is 90: 1 with PSNR 40 dB. Numéro de notice : A2004-187 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.816668 En ligne : https://doi.org/10.1109/TGRS.2003.816668 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26714
in IEEE Transactions on geoscience and remote sensing > vol 42 n° 4 (April 2004) . - pp 875 - 891[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-04041 RAB Revue Centre de documentation En réserve L003 Disponible Effect of grain size on remotely sensed spectral reflectance of sandy desert surfaces / G.S. Okin in Remote sensing of environment, vol 89 n° 3 (15/02/2004)
[article]
Titre : Effect of grain size on remotely sensed spectral reflectance of sandy desert surfaces Type de document : Article/Communication Auteurs : G.S. Okin, Auteur ; T.H. Painter, Auteur Année de publication : 2004 Article en page(s) : pp 272 - 280 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] désert
[Termes IGN] érosion éolienne
[Termes IGN] image AVIRIS
[Termes IGN] réflectance
[Termes IGN] sable
[Termes IGN] transfert radiatifRésumé : (Auteur) The effect of soil surface texture on spectral reflectance is reported for a site in the Mojave Desert. Abandoned central-pivot agricultural fields in the Manix Basin of southeastern California have introduced deflationary surface, into the otherwise stable, armored desert surface. This has resulted in sand plumes, eroded from the fields by wind, transported by saltation and deposited downwind of the fields. Grain size analysis of this wind-transported material reveals a fractionation by size within the plume, with smaller effective particle size toward the toe of the plume. This fractionation results from the greater mobility of smaller particles, and the longer saltation paths they take once airborne. Radiative transfer modeling of quartz grains with absorbing rinds indicates that the difference in grain size observed in the field should be revealed in apparent surface reflectance and are resolvable within the noise-equivalent delta-reflectance of the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) instrument. Analysis of AVIRIS-derived apparent surface reflectance demonstrates the expected negative correlation between effective grain size of sand in the plume and reflectance, with the most significant correlations in the short-wave infrared. The change in reflectance per mm change in particle diameter was - 0.06 at & ~ 1.7 um and - 0.08 at & ~ 2.2 um with R2 = 0.89 and 0.93, respectively. Numéro de notice : A2004-018 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.10.008 En ligne : https://doi.org/10.1016/j.rse.2003.10.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26546
in Remote sensing of environment > vol 89 n° 3 (15/02/2004) . - pp 272 - 280[article]Unsupervised classification of hyperspectral data: an ICA mixture model based approach / Chintan A. Shah in International Journal of Remote Sensing IJRS, vol 25 n° 2 (January 2004)PermalinkHyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data / N. Oppelt in International Journal of Remote Sensing IJRS, vol 25 n° 1 (January 2004)PermalinkAVIRIS measurements of chlorophyll, suspended minerals, dissolved organic carbon, and turbidity in the Neuse River, North Carolina / M.A. Karaska in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 1 (January 2004)PermalinkSpectral reflectance characterization of shallow lakes from the Brazilian pantanal wetlands with field and airborne hyperspectral data / L.S. Galvao in International Journal of Remote Sensing IJRS, vol 24 n° 21 (November 2003)PermalinkSpectral resolution requirements for mapping urban areas / Martin Herold in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkAnalysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: Comparison between an Airborne (AVIRIS) and a spaceborne (Hyperion) sensor / M.L. Smith in IEEE Transactions on geoscience and remote sensing, vol 41 n° 6 (June 2003)PermalinkFusion of hyperspectral and radar data using the IHS transformation to enhance urban surface features / C.M. Chen in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 1-2 (June - December 2003)PermalinkHyperspectral texture recognition using a multiscale opponent representation / M. Shi in IEEE Transactions on geoscience and remote sensing, vol 41 n° 5 (May 2003)PermalinkRadiative transfer codes applied to hyperspectral data for the retrieval of surface reflectance / K. Staenz in ISPRS Journal of photogrammetry and remote sensing, vol 57 n° 3 (December 2002 - January 2003)PermalinkBest-bases feature extraction algorithms for classification of hyperspectral data / Satish Kumar in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkDetection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California / M. Garcia in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkA new search algorithm for feature selection in hyperspectral remote sensing images / S.B. Serpico in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkEtude des relations spécifiques entre végétation et roches du massif de Ronda par télédétection hyperspectrale AVIRIS (1991) et HYMAP (2000) / Guillaume Hallereau (2001)PermalinkEtudes des mélanges spectraux visible et infrarouge pour la cartographie pétrologique sur images Aviris / N. Goulli (1998)PermalinkApport de la modélisation du transfert radiatif pour l'étude des écosystèmes forestiers par télédétection / V. Pinel (1997)PermalinkPermalinkSpatial interference in the AVIRIS imaging spectrometer / J.F. Rose in Photogrammetric Engineering & Remote Sensing, PERS, vol 55 n° 9 (september 1989)Permalink