Centre de documentation
scientifique

Dépouillements

On diverse noises in hyperspectral unmixing / Chunzhi Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5388 - 5402 Titre : On diverse noises in hyperspectral unmixing Type de document : Article/Communication Auteurs : Chunzhi Li, Auteur ; Xiaohua Chen, Auteur ; Yunliang Jiang, Auteur Année de publication : 2015 Article en page(s) : pp 5388 - 5402 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] bruit (théorie du signal) [Termes IGN] erreur aléatoire [Termes IGN] factorisation de matrice non-négative [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale Résumé : (Auteur) Traditional spectral unmixing methods are usually based on the linear mixture model (LMM) or nonlinear mixture model (NLMM), in which only the additive noise is considered. However, in hyperspectral applications, the additive, multiplicative, and mixed noises play important roles. In this paper, we propose an antinoise model for hyperspectral unmixing. In the antinoise model, all the additive, multiplicative and mixed noises are addressed. To deal with the problems faced by LMM or NLMM and to tackle the antinoise model, an antinoise model based hyperspectral unmixing method is presented, where block coordinate descent is employed to solve an approximated L0 norm constraint, then a nonnegative matrix factorization (NMF) method is presented, which is based on the bounded Itakura-Saito divergence. The experimental results on both synthetic and real hyperspectral data sets demonstrate the efficacy of the proposed model and the corresponding method. Numéro de notice : A2015-751 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421993 Date de publication en ligne : 01/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421993 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78739 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

A novel MKL model of integrating LiDAR data and MSI for urban area classification / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5312 - 5326 Titre : A novel MKL model of integrating LiDAR data and MSI for urban area classification Type de document : Article/Communication Auteurs : Yanfeng Gu, Auteur ; Qingwang Wang, Auteur ; Xiuping Jia, Auteur ; Jón Alti, Auteur Année de publication : 2015 Article en page(s) : pp 5312 - 5326 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] classificateur [Termes IGN] classification à base de connaissances [Termes IGN] classification automatique [Termes IGN] données lidar [Termes IGN] image multibande [Termes IGN] image spectrale [Termes IGN] milieu urbain Résumé : (Auteur) A novel multiple-kernel learning (MKL) model is proposed for urban classification to integrate heterogeneous features (HF-MKL) from two data sources, i.e., spectral images and LiDAR data. The features include spectral, spatial, and elevation attributes of urban objects from the two data sources. With these heterogeneous features (HFs), the new MKL model is designed to carry out feature fusion that is embedded in classification. First, Gaussian kernels with different bandwidths are used to measure the similarity of samples on each feature at different scales. Then, these multiscale kernels with different features are integrated using a linear combination. In the combination, the weights of the kernels with different features are determined by finding a projection based on the maximum variance. This way, the discriminative ability of the HFs is exploited at different scales and is also integrated to generate an optimal combined kernel. Finally, the optimization of the conventional support vector machine with this kernel is performed to construct a more effective classifier. Experiments are conducted on two real data sets, and the experimental results show that the HF-MKL model achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms. Numéro de notice : A2015-752 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421051 Date de publication en ligne : 07/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421051 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78742 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

A semiautomated probabilistic framework for tree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture / S. Basu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5690 - 5708 Titre : A semiautomated probabilistic framework for tree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture Type de document : Article/Communication Auteurs : S. Basu, Auteur ; Sangram Ganguly, Auteur ; Ramakrishna R. Nemani, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 5690 - 5708 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] architecture des systèmes d'information [Termes IGN] classification non dirigée [Termes IGN] couvert forestier [Termes IGN] Etats-Unis [Termes IGN] réseau neuronal artificiel [Termes IGN] segmentation d'image Résumé : (Auteur) Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps. Numéro de notice : A2015-753 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2428197 Date de publication en ligne : 26/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2428197 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78743 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

Multiangle BSAR imaging based on BeiDou-2 navigation satellite system: experiments and preliminary results / Tao Zeng in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5760 - 5773 Titre : Multiangle BSAR imaging based on BeiDou-2 navigation satellite system: experiments and preliminary results Type de document : Article/Communication Auteurs : Tao Zeng, Auteur ; Dongyang Ao, Auteur ; Cheng Hu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 5760 - 5773 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] acquisition d'images [Termes IGN] fusion d'images [Termes IGN] image radar moirée [Termes IGN] positionnement par BeiDou [Termes IGN] positionnement par GNSS [Termes IGN] radar bistatique [Termes IGN] zone d'intérêt Résumé : (Auteur) This paper analyzes the multiangle imaging results for bistatic synthetic aperture radar (BSAR) based on global navigation satellite systems (GNSS-BSAR). Due to the shortcoming of GNSS-BSAR images, a multiangle observation and data processing strategy based on BeiDou-2 navigation satellites was put forward to improve the quality of images and the value of system application. Twenty-six BSAR experiments were conducted and analyzed in different configurations. Furthermore, a region-based fusion algorithm using region-of-interest (ROI) segmentation was proposed to generate a high-quality fusion image. Based on the fusion image, typical targets such as water area, vegetation area, and artificial targets were compared and interpreted among single/multiple-angle images. The results reveal that the multiangle imaging method was a good technique to enhance image information, which might extend the applications of GNSS-BSAR. Numéro de notice : A2015-754 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2430312 Date de publication en ligne : 26/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2430312 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78744 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

Forest height estimation by means of Pol-InSAR data inversion : The role of the vertical wavenumber / Florian Kugler in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5294 - 5311 Titre : Forest height estimation by means of Pol-InSAR data inversion : The role of the vertical wavenumber Type de document : Article/Communication Auteurs : Florian Kugler, Auteur ; Seung-Kuk Lee, Auteur ; Irena Hajnsek, Auteur ; Konstantinos P. Papathanassiou, Auteur Année de publication : 2015 Article en page(s) : pp 5294 - 5311 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] données polarimétriques [Termes IGN] estimation statistique [Termes IGN] forêt [Termes IGN] hauteur des arbres [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] inversion [Termes IGN] Pol-INSAR [Termes IGN] polarimétrie radar [Termes IGN] validation des données Résumé : (Auteur) This paper examines the multifaceted effect of the effective spatial baseline, as expressed through the vertical (interferometric) wavenumber, on the inversion of forest height from polarimetric interferometric synthetic aperture radar (Pol-InSAR) data. First, the role of the vertical wavenumber in relating forest height to the interferometric (volume) coherence is introduced. Through the review of the forest height inversion from Pol-InSAR data, the effect of the vertical wavenumber on the inversion performance is evaluated. The selection of optimum with respect to forest height inversion performance, vertical wavenumbers is discussed. The impact of the acquisition geometry and terrain slopes on the vertical wavenumber and their consideration in the inversion methodology is addressed. The individual effects discussed are demonstrated by means of airborne repeat pass Pol-InSAR acquisitions in L- and P-band acquired over different forest conditions, including a boreal, a temperate, and a tropical forest test site. The achieved forest height inversion performance is validated against reference height data derived from airborne LIDAR acquisitions. Numéro de notice : A2015-747 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2420996 Date de publication en ligne : 04/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2420996 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78755 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data / Virpi Junttila in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5600 - 5612 Titre : Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data Type de document : Article/Communication Auteurs : Virpi Junttila, Auteur ; Tuomo Kauranne, Auteur ; Andrew O. Finley, Auteur ; John B. Bradford, Auteur Année de publication : 2015 Article en page(s) : pp 5600 - 5612 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] appariement d'images [Termes IGN] décomposition d'image [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle linéaire [Termes IGN] placette d'échantillonnage [Termes IGN] précision des données [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%-15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model's lack of fit. Numéro de notice : A2015-748 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2425916 Date de publication en ligne : 14/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2425916 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78757 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

Efficient superpixel-level multitask joint sparse representation for hyperspectral image classification / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5338 - 5351 Titre : Efficient superpixel-level multitask joint sparse representation for hyperspectral image classification Type de document : Article/Communication Auteurs : Jiayi Li, Auteur ; Hongyan Zhang, Auteur ; Liangpei Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 5338 - 5351 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse infrapixellaire [Termes IGN] apprentissage automatique [Termes IGN] classification [Termes IGN] données clairsemées [Termes IGN] image hyperspectrale [Termes IGN] représentation des données Résumé : (Auteur) In this paper, we propose a superpixel-level sparse representation classification framework with multitask learning for hyperspectral imagery. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and the correlation and distinctiveness of pixels in a spatial local region. Compared with some of the state-of-the-art hyperspectral classifiers, the superiority of the multiple-feature combination, the spatial prior utilization, and the computational complexity are maintained at the same time in the proposed method. The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparse (collaborative) representation-based algorithms and some popular hyperspectral multiple-feature classifiers. Numéro de notice : A2015-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421638 Date de publication en ligne : 29/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78758 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5352 - 5376 Titre : Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm Type de document : Article/Communication Auteurs : Stelios Mylonas, Auteur ; Dimitris G. Stavrakoudis, Auteur ; John B. Theocharis, Auteur ; Paris A. Mastorocostas, Auteur Année de publication : 2015 Article en page(s) : pp 5352 - 5376 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme génétique [Termes IGN] classification floue [Termes IGN] classification spectrale [Termes IGN] regroupement de données [Termes IGN] segmentation d'image Résumé : (Auteur) In this paper, we propose an integrated framework of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the object's extraction. Furthermore, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets. Numéro de notice : A2015-750 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421640 Date de publication en ligne : 08/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421640 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78759 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

MAGI : A new high-performance airborne thermal-infrared imaging spectrometer for earth science applications / Jeffrey L. Hall in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5447 - 5457 Titre : MAGI : A new high-performance airborne thermal-infrared imaging spectrometer for earth science applications Type de document : Article/Communication Auteurs : Jeffrey L. Hall, Auteur ; Richard H. Boucher, Auteur ; Kerry N. Buckland, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 5447 - 5457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] capteur aérien [Termes IGN] détection de cible [Termes IGN] gaz [Termes IGN] image thermique [Termes IGN] rayonnement infrarouge thermique [Termes IGN] roche [Termes IGN] spectromètre imageur Résumé : (Auteur) A new airborne facility instrument for Earth science applications is introduced. The Mineral and Gas Identifier (MAGI) is a wide-swath (programmable up to ±42° off nadir) moderate spectral resolution thermal-infrared (TIR) imaging spectrometer that spans the 7.1- to 12.7-μm spectral window in 32 uniform and contiguous channels. Its spectral resolution enables improved discrimination of rock and mineral types, greatly expanded gas-detection capability, and generally more accurate land-surface temperature retrievals. The instrument design arose from trade studies between spectral resolution, spectral range, and instrument sensitivity and has now been validated by flight data acquired with the completed sensor. It offers a potential prototype for future space-based TIR instruments, which will require much higher spectral resolution than is currently available in order to address more detailed climate, anthropogenic, and solid Earth science questions. Numéro de notice : A2015-755 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2422817 Date de publication en ligne : 11/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2422817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78762 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible