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HYPXIM, an innovative spectroimager for science, security and defence requirements / M.J. Lefevre-Fonollosa in Revue Française de Photogrammétrie et de Télédétection, n° 200 (Novembre 2012)
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Titre : HYPXIM, an innovative spectroimager for science, security and defence requirements Type de document : Article/Communication Auteurs : M.J. Lefevre-Fonollosa, Auteur ; S. Michel, Auteur ; S. Hosford, Auteur Année de publication : 2012 Article en page(s) : pp 20 - 27 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Missions spatiales
[Termes IGN] acquisition d'images
[Termes IGN] acquisition de données
[Termes IGN] bande infrarouge
[Termes IGN] HYPXIM
[Termes IGN] image à haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] plateformeRésumé : (Auteur) This paper provides an overview of hyperspectral applications and data requirements gathered by an ad-hoc group of French scientists and Defence users. This group known by the acronym GSH (Groupe de Synthese sur I'Hyperspectral) has addressed clear and detailed technical requirements for a high spatial resolution hyperspectral mission on the following themes: study of vegetation, coastal and inland water ecosystems, geosciences, urban environment, atmospheric studies, security, and Defence. The synthesis of these requirements substantially helped to set up consolidated space-based system requirements (i.e. mission requirements) in terms of spectral domain, spectral resolution, signal-to-noise ratio, spatial resolution, swath and revisiting period, which revealed the main key drivers for the design of a very innovative hyperspectral space instrument. HYPXIM is a new-generation hyperspectral concept which meets the needs of a wide community of users in the world. During the phase 0, CNES with the support of industry (Astrium et Thales Alenia Space) has compared two different scenarii. The first scenario, named HYPXIM-C, aims at finding out the highest possible resolution level (15 m) achievable using a microsatellite platform, whereas the goals of the second scenario, called HYPXIM-P, are to reach a higher spatial resolution (8 m), and to provide a TIR hyperspectral capability. The HYPXIM phase A was recently decided and focused on the most performing concept, but without TIR capabilities. The challenges for the selected HYPXIM mission were to design an agil high resolution spectroimager on a mini-satellite. Preliminary studies with industrial support show that this challenge can be taken to space around 2020/21 depending on the development of critical technologies (like specific detectors). Expected lifetime in orbit is 10 years, including end-of-life operations. Numéro de notice : A2012-564 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.52638/rfpt.2012.58 En ligne : https://doi.org/10.52638/rfpt.2012.58 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32010
in Revue Française de Photogrammétrie et de Télédétection > n° 200 (Novembre 2012) . - pp 20 - 27[article]Total variation spatial regularization for sparse hyperspectral unmixing / M. Iordache in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)
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Titre : Total variation spatial regularization for sparse hyperspectral unmixing Type de document : Article/Communication Auteurs : M. Iordache, Auteur ; J. Biuoucas-Dias, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2012 Article en page(s) : pp 4484 - 4502 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] image hyperspectrale
[Termes IGN] information géographique
[Termes IGN] prise en compte du contexte
[Termes IGN] régressionRésumé : (Auteur) Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures (also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral imaging instrument. In recent work, the linear spectral unmixing problem has been approached in semisupervised fashion as a sparse regression one, under the assumption that the observed image signatures can be expressed as linear combinations of pure spectra, known a priori and available in a library. It happens, however, that sparse unmixing focuses on analyzing the hyperspectral data without incorporating spatial information. In this paper, we include the total variation (TV) regularization to the classical sparse regression formulation, thus exploiting the spatial-contextual information present in the hyperspectral images and developing a new algorithm called sparse unmixing via variable splitting augmented Lagrangian and TV. Our experimental results, conducted with both simulated and real hyperspectral data sets, indicate the potential of including spatial information (through the TV term) on sparse unmixing formulations for improved characterization of mixed pixels in hyperspectral imagery. Numéro de notice : A2012-588 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2191590 Date de publication en ligne : 07/05/2012 En ligne : https://doi.org/ 10.1109/TGRS.2012.2191590 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32034
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 11 Tome 1 (November 2012) . - pp 4484 - 4502[article]Triangular factorization-based simplex algorithms for hyperspectral unmixing / W. Xia in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)
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Titre : Triangular factorization-based simplex algorithms for hyperspectral unmixing Type de document : Article/Communication Auteurs : W. Xia, Auteur ; H. Pu, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 4420 - 4440 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] algorithme du simplexe
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] factorisation
[Termes IGN] image hyperspectrale
[Termes IGN] programmation linéaireRésumé : (Auteur) In the linear unmixing of hyperspectral images, the observation pixels form a simplex whose vertices correspond to the endmembers, hence finding the endmembers is equivalent to extracting these vertices. A common technique for determining vertices is to analyze the simplex volume, but it usually has a high computational complexity, resulting from the exhaustive searching of volume in the large hyperspectral data. This problem limits the practicability and real-time application. In this paper, we utilize triangular factorization (TF) to calculate the volume, deducing a method named simplex volume analysis based on TF (SVATF). It requires just one comparison through the data to succeed in finding the global optimal solution for all the endmembers, thus improving the searching efficiency. Dimensionality reduction transformation is not necessary, which is another advantage of this method. Moreover, since TF is a broad conception including different methods, SVATF is a framework including various implementations. Based on TF, we also propose a fast learning algorithm named abundance quantification based on TF to estimate the abundances, which further saves the computation by utilizing the intermediate values involved in SVATF. The abundance estimation method can rectify possible errors in the given endmembers by utilizing two important constraints (abundance nonnegative constraint and abundance sum-to-one constraint) of the linear mixture model, so it is useful for the imagery without pure pixels. Experimental results on synthetic and real hyperspectral data demonstrate that the proposed methods can obtain accurate results with much lower computational complexity, with respect to other state-of-the-art methods. Numéro de notice : A2012-587 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2195185 Date de publication en ligne : 22/05/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2195185 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32033
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 11 Tome 1 (November 2012) . - pp 4420 - 4440[article]A vector sift detector for interest point detection in hyperspectral imagery / L. Dorado-Munoz in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)
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Titre : A vector sift detector for interest point detection in hyperspectral imagery Type de document : Article/Communication Auteurs : L. Dorado-Munoz, Auteur ; M. Velez-Reys, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 4521 - 4533 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] données vectorielles
[Termes IGN] extraction automatique
[Termes IGN] image AISA+
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] point d'intérêt
[Termes IGN] SIFT (algorithme)Résumé : (Auteur) This paper presents an algorithm for the extraction of interest points in hyperspectral images. Interest points are spatial features of the image that capture information from their neighbors, are distinctive and stable under transformations such as translation and rotation, are helpful in data reduction, and reduce the computational burden of various algorithms such as image registration by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. Interest points have been applied to problems in computer vision, including image matching, recognition, 3-D reconstruction, and change detection. Interest point operators for monochromatic images were proposed more than a decade ago and have extensively been studied. An interest point operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, and stable under geometric transformations. An extension of Lowe's scale-invariant feature transform (SIFT) to vector images is proposed here. The approach takes the vectorial nature of the hyperspectral images into account. Furthermore, the multiscale representation of the image is generated by vector nonlinear diffusion, which leads to improved detection, because it better preserves edges in the image as opposed to Gaussian blurring, which is used in Lowe's original approach. Experiments with hyperspectral images of the same and different resolutions that were collected with the Airborne Hyperspectral Imaging System (AISA) and Hyperion sensors are presented. Evaluation of the proposed approach using repeatability criterion and image registration is carried out. Comparisons with other approaches that were described in the literature are presented. Numéro de notice : A2012-591 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2191791 Date de publication en ligne : 09/05/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2191791 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32037
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 11 Tome 1 (November 2012) . - pp 4521 - 4533[article]Dimensionality reduction of hyperspectral data using spectral fractal feature / K. Mukherjee in Geocarto international, vol 27 n° 6 (October 2012)
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Titre : Dimensionality reduction of hyperspectral data using spectral fractal feature Type de document : Article/Communication Auteurs : K. Mukherjee, Auteur ; J. Ghosh, Auteur ; R. Mittal, Auteur Année de publication : 2012 Article en page(s) : pp 515 - 531 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] courbe
[Termes IGN] dimension fractale
[Termes IGN] image hyperspectrale
[Termes IGN] réduction
[Termes IGN] réponse spectraleRésumé : (Auteur) A new approach for dimensionality reduction of hyperspectral data has been proposed in this article. The method is based on extraction of fractal-based features from the hyperspectral data. The features have been generated using spectral fractal dimension of the spectral response curves (SRCs) after smoothing, interpolating and segmenting the curves. The new features so generated have then been used to classify hyperspectral data. Comparing the post classification accuracies with some other conventional dimensionality reduction methods, it has been found that the proposed method, with less computational complexity than the conventional methods, is able to provide classification accuracy statistically equivalent to those from conventional methods. Numéro de notice : A2012-512 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.642411 Date de publication en ligne : 11/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.642411 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31958
in Geocarto international > vol 27 n° 6 (October 2012) . - pp 515 - 531[article]Réservation
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