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Noise simulation and correction in synthetic airborne TIR Data for mineral quantification / Christoph Hecker in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
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Titre : Noise simulation and correction in synthetic airborne TIR Data for mineral quantification Type de document : Article/Communication Auteurs : Christoph Hecker, Auteur ; Dean Riley, Auteur ; Mark Van Der Meijde, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1545 - 1553 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] erreur systématique
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image thermique
[Termes IGN] prospection minérale
[Termes IGN] quartz
[Termes IGN] rapport signal sur bruit
[Termes IGN] rayonnement infrarouge thermique
[Termes IGN] régression
[Termes IGN] simulationRésumé : (Auteur) Rock-forming minerals (such as feldspar and quartz) can be identified and quantified from thermal infrared (TIR) laboratory spectroscopy using spectral models. This paper uses synthetic airborne TIR spectra to test whether the hyperspectral Spatially Enhanced Broadband Array Spectrograph System (SEBASS) would theoretically be able to detect quartz and feldspar minerals and quantitatively predict mineral modes in felsic igneous rocks. Data from a previous laboratory study were used to simulate TIR spectra with band locations and noise levels of the SEBASS sensor. The quantitative partial least squares regression (PLSR) models from that study were applied to newly created synthetic SEBASS data, and results were compared with the predictions from the previous study. Predicted compositions based on SEBASS band positions are nearly identical (ρ = 0.995) to those based on laboratory resolution. Results are still reliable [prediction errors within 0.4% (absolute)] to the original laboratory PLSR predictions when adding up to 1% noise (about five times the SEBASS noise level) to the synthetic data. Prediction errors rapidly increase when noise levels beyond 1% are used. These results show that SEBASS' spectral resolution, spectral coverage, and signal-to-noise levels are sufficient to quantitatively predict quartz and feldspar amounts, and feldspar compositions with models based on PLSR. Spectral distortions, such as reduced spectral contrast, tilts, and vertical shifts, must be compensated for before these quantitative models are applied. A mean and standard deviation (MASD) normalization is proposed using a set of ground data for compensating systematic errors that are common to all image pixels. Numéro de notice : A2016-124 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2482386 En ligne : https://doi.org/10.1109/TGRS.2015.2482386 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80005
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1545 - 1553[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016031 SL Revue Centre de documentation Revues en salle Disponible A penalized spline-based attitude model for high-resolution satellite imagery / Hongbo Pan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
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Titre : A penalized spline-based attitude model for high-resolution satellite imagery Type de document : Article/Communication Auteurs : Hongbo Pan, Auteur ; Zheng-Rong Zou, Auteur ; Guo Zhang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1849 - 1859 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] filtrage du bruit
[Termes IGN] fonction spline
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] image ZiYuan-3
[Termes IGN] orientationRésumé : (Auteur) Attitude models play a prominent role in the geometric processing of high-resolution satellite imagery (HRSI). Because of the high accuracy of the matching algorithm, attitude oscillations can occur in HRSI. Various methods for correcting this attitude oscillation with parallax observations have been proposed. However, few researchers have attempted to model the oscillation from the attitude records or have taken noise into consideration. In this paper, a penalized spline-based attitude model is proposed, which can model the oscillation with piecewise and continuously differentiable polynomials and smooth out the attitude noise with a penalty function. The balance between the fitting accuracy and noise smoothing is controlled by a penalty parameter, which is estimated by generalized cross-validation. Given that the attitude error introduces distortions into sensor-corrected images, the band-to-band registration of multispectral images is used to validate the attitude model. Five multispectral data sets captured by ZiYuan-3 are used to demonstrate the effectiveness of the proposed method. Compared with third-degree polynomials and cubic spline interpolation, the penalized spline model delivers the best performance by limiting the misregistration caused by the attitude model to within 0.1 pixels. Numéro de notice : A2016-128 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2489382 En ligne : https://doi.org/10.1109/TGRS.2015.2489382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80015
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1849 - 1859[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016031 SL Revue Centre de documentation Revues en salle Disponible Uniformity-based superpixel segmentation of hyperspectral images / Arun M. Saranathan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
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Titre : Uniformity-based superpixel segmentation of hyperspectral images Type de document : Article/Communication Auteurs : Arun M. Saranathan, Auteur ; Mario Parente, Auteur Année de publication : 2016 Article en page(s) : pp 1419 - 1430 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse infrapixellaire
[Termes IGN] données géologiques
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'imageRésumé : (Auteur) Superpixel segmentation algorithms attempt to group contiguous image pixels which are in homogeneous regions into segments (superpixels). Superpixel segmentation maps have proven successful in improving the performance of unmixing algorithms on hyperspectral images. For hyperspectral images (HSIs), segment members must contain spectrally similar pixels, a requirement we refer to as segment uniformity. Existing superpixel segmentation algorithms which have been applied to HSIs provide no guarantees on the uniformity inside segments. In the absence of such guarantees, the only viable option is to make the segments small enough that uniformity is always ensured; this leads to an oversegmentation of the image. An accurate uniformity measure would lead to a more accurate segmentation. We propose a graph-based agglomerative approach that enforces segment uniformity by setting a threshold for maximum variability inside segments. The threshold is computed by a statistical analysis of the within-class and between-class spectral divergences of several mineral families of interest. We show that the proposed algorithm can be used to generate parsimonious segmentations and facilitate the computation of accurate mineralogical summaries for several simulated and real HSIs of terrestrial and planetary geological surfaces. Numéro de notice : A2016-123 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2480863 En ligne : https://doi.org/10.1109/TGRS.2015.2480863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80003
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1419 - 1430[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016031 SL Revue Centre de documentation Revues en salle Disponible Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
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Titre : Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion Type de document : Article/Communication Auteurs : Renlong Hang, Auteur ; Qingshan Liu, Auteur ; Huihui Song, Auteur ; Yubao Sun, Auteur Année de publication : 2016 Article en page(s) : pp 783 - 794 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification multibande
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] image hyperspectrale
[Termes IGN] matriceRésumé : (Auteur) Spatial-spectral feature fusion is well acknowledged as an effective method for hyperspectral (HS) image classification. Many previous studies have been devoted to this subject. However, these methods often regard the spatial-spectral high-dimensional data as 1-D vector and then extract informative features for classification. In this paper, we propose a new HS image classification method. Specifically, matrix-based spatial-spectral feature representation is designed for each pixel to capture the local spatial contextual and the spectral information of all the bands, which can well preserve the spatial-spectral correlation. Then, matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a random sampling technique is used to produce a subspace ensemble for final HS image classification. Experiments are conducted on three HS remote sensing data sets acquired by different sensors, and experimental results demonstrate the efficiency of the proposed method. Numéro de notice : A2016-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2465899 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2465899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79996
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 783 - 794[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016021 SL Revue Centre de documentation Revues en salle Disponible Telespazio aurait-il trouvé la solution pour développer l'usage du spatial / Françoise de Blomac in DécryptaGéo le mag, n° 174 (février 2016)
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Titre : Telespazio aurait-il trouvé la solution pour développer l'usage du spatial Type de document : Article/Communication Auteurs : Françoise de Blomac, Auteur Année de publication : 2016 Article en page(s) : pp 16 - 17 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] acquisition de données
[Termes IGN] carte thématique
[Termes IGN] drone
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] image spatiale
[Termes IGN] surveillance de la végétation
[Termes IGN] surveillance du littoral
[Termes IGN] traitement de données localiséesRésumé : (auteur) Démocratiser l'imagerie satellitaire ? Tout le monde en rêve mais beaucoup s'y cassent les dents. Nicolas Vincent, vice-président de Téléspazio France, nous explique comment son entreprise a développé EarthLab, un réseau de bouquets de services exploitant la géo-information. En misant sur la dimension industrielle, le spécialiste du radar est -il en train de réussir là où beaucoup ont échoué ? Numéro de notice : A2016-052 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79648
in DécryptaGéo le mag > n° 174 (février 2016) . - pp 16 - 17[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 286-2016021 RAB Revue Centre de documentation En réserve L003 Disponible PermalinkAcquisition et reconstruction de données 3D denses sous-marines en eau peu profonde par des robots d'exploration / Loïca Avanthey (2016)
PermalinkPermalinkApport de la prise en compte de la variabilité intra-classe dans les méthodes de démélange hyperspectral pour l'imagerie urbaine / Charlotte Revel (2016)
PermalinkPermalinkPermalinkContributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques / Saadallah El Asmar (2016)
PermalinkPermalinkDevelopment of a SGM-based multi-view reconstruction framework for aerial imagery / Mathias Rothermel (2016)
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