Descripteur
Documents disponibles dans cette catégorie (1241)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Using landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas / Cooper McCann in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
![]()
[article]
Titre : Using landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas Type de document : Article/Communication Auteurs : Cooper McCann, Auteur ; Kevin S. Repasky, Auteur ; Mikindra Morin, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 5002 - 5014 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat
[Termes IGN] image multibande
[Termes IGN] mosaïquage d'images
[Termes IGN] paturage
[Termes IGN] qualité radiométrique (image)
[Termes IGN] réflectance de surfaceRésumé : (Auteur) Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Through selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km2 acquired on June 21, 2014 and June 24, 2015 are presented. Data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation. Numéro de notice : A2017-665 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2699618 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2699618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87102
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5002 - 5014[article]Colour Helmholtz stereopsis for reconstruction of dynamic scenes with arbitrary unknown reflectance / Nadejda Roubtsova in International journal of computer vision, vol 124 n° 1 (August 2017)
![]()
[article]
Titre : Colour Helmholtz stereopsis for reconstruction of dynamic scenes with arbitrary unknown reflectance Type de document : Article/Communication Auteurs : Nadejda Roubtsova, Auteur ; Jean-Yves Guillemaut, Auteur Année de publication : 2017 Article en page(s) : pp 18 – 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chromatopsie
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] image multibande
[Termes IGN] longueur d'onde
[Termes IGN] multiplexage
[Termes IGN] reconstruction d'image
[Termes IGN] réflectanceRésumé : (auteur) Helmholtz Stereopsis is a powerful technique for reconstruction of scenes with arbitrary reflectance properties. However, previous formulations have been limited to static objects due to the requirement to sequentially capture reciprocal image pairs (i.e. two images with the camera and light source positions mutually interchanged). In this paper, we propose colour Helmholtz Stereopsis—a novel framework for framework based on wavelength multiplexing. To address the new set of challenges introduced by multispectral data acquisition, the proposed Colour Helmholtz Stereopsis pipeline uniquely combines a tailored photometric calibration for multiple camera/light source pairs, a novel procedure for spatio-temporal surface chromaticity calibration and a state-of-the-art Bayesian formulation necessary for accurate reconstruction from a minimal number of reciprocal pairs. In this framework, reflectance is spatially unconstrained both in terms of its chromaticity and the directional component dependent on the illumination incidence and viewing angles. The proposed approach for the first time enables modelling of dynamic scenes with arbitrary unknown and spatially varying reflectance using a practical acquisition set-up consisting of a small number of cameras and light sources. Experimental results demonstrate the accuracy and flexibility of the technique on a variety of static and dynamic scenes with arbitrary unknown BRDF and chromaticity ranging from uniform to arbitrary and spatially varying. Numéro de notice : A2017-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-016-0951-0 En ligne : https://doi.org/10.1007/s11263-016-0951-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85935
in International journal of computer vision > vol 124 n° 1 (August 2017) . - pp 18 – 48[article]From subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
![]()
[article]
Titre : From subpixel to superpixel : a novel fusion framework for hyperspectral image classification Type de document : Article/Communication Auteurs : Ting Lu, Auteur ; Shutao Li, Auteur ; Leyuan Fang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4398 - 4411 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] classification pixellaire
[Termes IGN] combinaison linéaire
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) Supervised classification of hyperspectral images (HSI) is a very challenging task due to the existence of noisy and mixed spectral characteristics. Recently, the widely developed spectral unmixing techniques offer the possibility to extract spectral mixture information at a subpixel level, which can contribute to the categorization of seriously mixed spectral pixels. Besides, it has been demonstrated that the discrimination between different materials will be improved by integrating the geometry and structure information, which can be derived from the variance between neighboring pixels. Furthermore, by incorporating the spatial context, the superpixel-based spectral-spatial similarity information can be used to smooth classification results in homogeneous regions. Therefore, a novel fusion framework for HSI classification that combines subpixel, pixel, and superpixel-based complementary information is proposed in this paper. Here, both feature fusion and decision fusion schemes are introduced. For the feature fusion scheme, the first step is to extract subpixel-level, pixel-level, and superpixel-level features from HSI, respectively. Then, the multiple feature-induced kernels are fused to form one composite kernel, which is incorporated with a support vector machine (SVM) classifier for label assignment. For the decision fusion scheme, class probabilities based on three different features are estimated by the probabilistic SVM classifier first. Then, the class probabilities are adaptively fused to form a probabilistic decision rule for classification. Experimental results tested on different real HSI images can demonstrate the effectiveness of the proposed fusion schemes in improving discrimination capability, when compared with the classification results relied on each individual feature. Numéro de notice : A2017-654 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2691906 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2691906 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86439
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4398 - 4411[article]Intersensor statistical matching for pansharpening : theoretical issues and practical solutions / Luciano Alparone in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
![]()
[article]
Titre : Intersensor statistical matching for pansharpening : theoretical issues and practical solutions Type de document : Article/Communication Auteurs : Luciano Alparone, Auteur ; Andrea Garzelli, Auteur ; Gemine Vivone, Auteur Année de publication : 2017 Article en page(s) : pp 4682 - 4695 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] appariement d'histogramme
[Termes IGN] appariement d'images
[Termes IGN] image Ikonos
[Termes IGN] image multibande
[Termes IGN] image multicapteur
[Termes IGN] image panchromatique
[Termes IGN] image Worldview
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] résolution multipleRésumé : (Auteur) In this paper, the authors investigate the statistical matching of the panchromatic (Pan) image to the multispectral (MS) bands, also known as the histogram matching, for the two main classes of pansharpening methods, i.e., those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods. Also, hybrid methods combining CS with MRA, like the widespread additive wavelet luminance proportional (AWLP), are investigated. It is shown that all spectral, spatial, and hybrid methods must perform a dynamics matching of the enhancing Pan to the individual MS bands for MRA or a combination of them (the component that shall be substituted) for CS. For hybrid methods, the problem is more complex and both types of histogram matching may be suitable. Such an intersensor balance may be either explicit or implicitly performed by the detail-injection model, e.g., the popular projective and multiplicative injection models. An experimental setup exploiting IKONOS and WorldView-2 data sets demonstrates that a correct histogram matching is the key to attain extra performance from established methods. As a first result of this paper, the AWLP method has been revisited and its performance significantly improved by simply performing the histogram matching of Pan to the individual MS bands, rather than to the intensity component, thereby losing the original proportionality feature. Numéro de notice : A2017-502 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2697943 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2697943 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86447
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4682 - 4695[article]Learning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
![]()
[article]
Titre : Learning and transferring deep joint spectral–spatial features for hyperspectral classification Type de document : Article/Communication Auteurs : Jingxiang Yang, Auteur ; Yong-Qiang Zhao, Auteur ; Jonathan Cheung-Wai Chan, Auteur Année de publication : 2017 Article en page(s) : pp 4729 - 4742 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] image ROSIS
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) Feature extraction is of significance for hyperspectral image (HSI) classification. Compared with conventional hand-crafted feature extraction, deep learning can automatically learn features with discriminative information. However, two issues exist in applying deep learning to HSIs. One issue is how to jointly extract spectral features and spatial features, and the other one is how to train the deep model when training samples are scarce. In this paper, a deep convolutional neural network with two-branch architecture is proposed to extract the joint spectral-spatial features from HSIs. The two branches of the proposed network are devoted to features from the spectral domain as well as the spatial domain. The learned spectral features and spatial features are then concatenated and fed to fully connected layers to extract the joint spectral-spatial features for classification. When the training samples are limited, we investigate the transfer learning to improve the performance. Low and mid-layers of the network are pretrained and transferred from other data sources; only top layers are trained with limited training samples extracted from the target scene. Experiments on Airborne Visible/Infrared Imaging Spectrometer and Reflective Optics System Imaging Spectrometer data demonstrate that the learned deep joint spectral-spatial features are discriminative, and competitive classification results can be achieved when compared with state-of-the-art methods. The experiments also reveal that the transferred features boost the classification performance. Numéro de notice : A2017-503 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2698503 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2698503 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86448
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4729 - 4742[article]Learning a discriminative distance metric with label consistency for scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkLearning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks / Shaohui Mei in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkMorphologically decoupled structured sparsity for rotation-invariant hyperspectral image analysis / Saurabh Prasad in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkA novel preunmixing framework for efficient detection of linear mixtures in hyperspectral images / Andrea Marinoni in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkPotential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas / Ram Avtar in Geocarto international, vol 32 n° 8 (August 2017)
PermalinkSuperpixel-based intrinsic image decomposition of hyperspectral images / Xudong Jin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkDeveloping detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor / Abel Chemura in Geocarto international, vol 32 n° 7 (July 2017)
PermalinkFusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area / Mohamed Barakat A. Gibril in Geocarto international, vol 32 n° 7 (July 2017)
PermalinkJoint hyperspectral superresolution and unmixing with interactive feedback / Chen Yi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)
PermalinkNorthern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)
Permalink