Descripteur
Termes IGN > sciences naturelles > physique > optique > optique physique
optique physiqueSynonyme(s)optique ondulatoire |
Documents disponibles dans cette catégorie (3085)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
An interactive tool for semi-automatic feature extraction of hyperspectral data / Zoltan Kovacs in Open geosciences, vol 8 n° 1 (January - July 2016)
[article]
Titre : An interactive tool for semi-automatic feature extraction of hyperspectral data Type de document : Article/Communication Auteurs : Zoltan Kovacs, Auteur ; Szilárd Szabó, Auteur Année de publication : 2016 Article en page(s) : pp 493 - 502 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction semi-automatique
[Termes IGN] image hyperspectrale
[Termes IGN] régression
[Termes IGN] spectrométrie
[Termes IGN] VBARésumé : (auteur) The spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in – Hyperspectral Data Analyst (HypDA) – for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing; i.e. to find the wavelengths responsible for separating data into predefined groups. HypDA can be used both with hyperspectral imagery and spectrometer measurements. This bivariate approach requires significantly fewer observations than popular multivariate methods; it can therefore be applied to a wide range of research areas. Numéro de notice : A2016--071 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/geo-2016-0040 En ligne : https://doi.org/10.1515/geo-2016-0040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84417
in Open geosciences > vol 8 n° 1 (January - July 2016) . - pp 493 - 502[article]Correction of atmospheric refraction geolocation error for high resolution optical satellite pushbroom images / Ming Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)
[article]
Titre : Correction of atmospheric refraction geolocation error for high resolution optical satellite pushbroom images Type de document : Article/Communication Auteurs : Ming Yan, Auteur ; Chengyi Wang, Auteur ; Jianglin Ma, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 427 - 435 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] correction atmosphérique
[Termes IGN] erreur de positionnement
[Termes IGN] géoréférencement direct
[Termes IGN] image à haute résolution
[Termes IGN] image DMC-3
[Termes IGN] image optique
[Termes IGN] image satellite
[Termes IGN] réfraction atmosphériqueRésumé : (Auteur) When an optical remote sensing satellite is imaging the Earth in-orbit, the propagation direction of the Line of Sight (LOS) will be changed because of atmospheric refraction. This will result in a geolocation deviation on the collinear rigorous geometric model for direct georeferencing, pushbroom images. To estimate and correct the atmospheric refraction geolocation error, the LOS vector tracking algorithm is introduced and a weighted mean algorithm is used to simplify the ISO standard atmospheric model into a troposphere and stratosphere, i.e., two layers spherical atmosphere. The simulation result shows the atmospheric refraction will introduce about 2 m and 7.5 m geometric displacement when the spacecraft is off-pointed view at 30 and 45 degree angle, respectively. For a state-of-the-art high resolution satellite, the atmospheric refraction displacement shall be corrected. The method has been practiced in the DMC3/TripleSat Constellation to remove the atmospheric refraction geolocation error without ground control points. Numéro de notice : A2016-441 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.427 En ligne : http://dx.doi.org/10.14358/PERS.82.6.427 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81346
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 427 - 435[article]A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery / Bei Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)
[article]
Titre : A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery Type de document : Article/Communication Auteurs : Bei Zhao, Auteur ; Yanfei Zhong, Auteur ; Liangpei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 73 – 85 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur paramétrique
[Termes IGN] classification automatique
[Termes IGN] classification dirigée
[Termes IGN] exitance spectrale
[Termes IGN] image à très haute résolution
[Termes IGN] mécanique statistique
[Termes IGN] modèle logique de donnéesRésumé : (auteur) Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral–structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes. Numéro de notice : A2016-579 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81718
in ISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 73 – 85[article]Exploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
[article]
Titre : Exploiting joint sparsity for pansharpening : the J-SparseFI algorithm Type de document : Article/Communication Auteurs : Xiao Xiang Zhu, Auteur ; Claas Grohnfeldt, Auteur ; Richard Bamler, Auteur Année de publication : 2016 Article en page(s) : pp 2664 - 2681 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de fusion
[Termes IGN] données clairsemées
[Termes IGN] fusion d'images
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] image Worldview
[Termes IGN] reconstruction d'image
[Termes IGN] régularisation de Tychonoff
[Termes IGN] réponse spectraleRésumé : (Auteur) Recently, sparse signal representation of image patches has been explored to solve the pansharpening problem. Although these proposed sparse-reconstruction-based methods lead to promising results, three issues remained unsolved: 1) high computational cost; 2) no consideration given to the possibility of mutually correlated information in different multispectral channels; and 3) requirement that the spectral responses of the panchromatic (Pan) image and the multispectral image cover the same wavelength range, which is not necessarily valid for most sensors. In this paper, we propose a sophisticated sparse image fusion algorithm, which is named “jointly sparse fusion of images” (J-SparseFI). It is based on the earlier proposed sparse fusion of images (SparseFI) algorithm and overcomes the aforementioned three drawbacks of the existing sparse image fusion algorithms. The computational problem is handled by reducing the problem size and by proposing a fully parallelizable scheme. Moreover, J-SparseFI exploits the possible signal structure correlations between multispectral channels by introducing the joint sparsity model (JSM) and sharpening the highly correlated adjacent multispectral channels together. This is done by exploiting the distributed compressive sensing theory that restricts the solution of an underdetermined system by considering an ensemble of signals being jointly sparse. J-SparseFI also offers a practical solution to overcome spectral range mismatch between the Pan and multispectral images. By means of sensor spectral response and channel mutual correlation analysis, the multispectral channels are assigned to primary groups of joint channels, secondary groups of joint channels, and individual channels. Primary groups of joint channels, individual channels, and secondary groups of joint channels are then reconstructed sequentially, by the JSM or by modified SparseFI, using a dictionary trained from the Pan image or previously reconstructed high-resolution multispectral channels. A recipe of how to choose appropriate algorithm parameters, including the most crucial regularization parameter, is provided. The algorithm is evaluated and validated using WorldView-2-like images that are simulated using very high resolution airborne HySpex hyperspectral imagery and further practically demonstrated using real WorldView-2 images. The algorithm's performance is compared with other state-of-the-art methods. Visual and quantitative analyses demonstrate the high quality of the proposed method. In particular, the analysis of the difference images suggests that J-SparseFI is superior in image resolution recovery. Numéro de notice : A2016-844 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2504261 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2504261 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82890
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2664 - 2681[article]Global sensitivity analysis of the L-MEB model for retrieving soil moisture / Zengyan Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
[article]
Titre : Global sensitivity analysis of the L-MEB model for retrieving soil moisture Type de document : Article/Communication Auteurs : Zengyan Wang, Auteur ; Tao Che, Auteur ; Yuei-An Liou, Auteur Année de publication : 2016 Article en page(s) : pp 2949 - 2962 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse de sensibilité
[Termes IGN] bande L
[Termes IGN] densité de la végétation
[Termes IGN] humidité du sol
[Termes IGN] rugosité du sol
[Termes IGN] température au solRésumé : (Auteur) A global sensitivity analysis utilizing the extended Fourier amplitude sensitivity test is used to determine the parameter sensitivity of the L-band microwave emission of the biosphere (L-MEB) model. The results are analyzed from two perspectives of calibration and inversion. First, the parameters of surface soil moisture, soil roughness factor, vegetation optical depth at nadir, and effective land surface temperature are the four most sensitive parameters in the L-MEB model, demonstrating their possibility to be retrieved in the multiparameter retrieval approaches. Then, the high total sensitivity index (TSI) values of surface soil temperature in the analyses emphasize the importance of high-precision land surface temperature data in the surface soil moisture retrievals, especially for rougher or more vegetated surface conditions. Finally, our analysis indicates that TSI values are high for the soil surface roughness and vegetation optical depth model parameters but low for the vegetation structure, single scattering albedo, and soil roughness coefficient model parameters at incidence angles near nadir. This suggests that calibration experiments performed at small incidence angles may be appropriate for some but not all of the model parameters, which characterize the effect of soil surface roughness and vegetation on the terrestrial brightness temperature. Consequently, new calibration procedures that account for the different relative sensitivities of these model parameters at larger incidence angles may need to be developed in the future. Numéro de notice : A2016-847 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2509176 En ligne : https://doi.org/10.1109/TGRS.2015.2509176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82928
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2949 - 2962[article]GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring / Erwan Motte in Sensors, vol 16 n° 5 (May 2016)PermalinkMultisensor and multispectral Lidar characterization and classification of a forest environment / Christopher Hopkinson in Canadian journal of remote sensing, vol 42 n° 5 ([01/05/2016])PermalinkRemote sensing of alpine glaciers in visible and infrared wavelengths: a survey of advances and prospects / Anshuman Bhardwaj in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)PermalinkStorm event representation and analysis based on a directed spatiotemporal graph model / W. Liu in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)PermalinkComparative analysis of real-time precise point positioning zenith total delay estimates / F.A. Ahmed in GPS solutions, vol 20 n° 2 (April 2016)PermalinkForest above ground biomass inversion by fusing GLAS with optical remote sensing data / Xiaohuan Xi in ISPRS International journal of geo-information, vol 5 n° 4 (April 2016)PermalinkThe attenuation of retroreflective signatures on surface soils / Robyn A. Barbato in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 4 (April 2016)Permalink14 years of GPS tropospheric delays in the French–Italian border region : comparisons and first application in a case study / Domenico Sguerso in Applied geomatics, vol 8 n° 1 (March 2016)PermalinkCorrection of terrestrial LiDAR intensity channel using Oren–Nayar reflectance model: An application to lithological differentiation / Dario Carrea in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)PermalinkDetermination of differential code biases with multi-GNSS observations / Ningbo Wang in Journal of geodesy, vol 90 n° 3 (March 2016)Permalink