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Denoising atmospheric radar signals using spectral-based subspace method applicable for PBS wind estimation / V.N. Sureshbabu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3853 - 3861 Titre : Denoising atmospheric radar signals using spectral-based subspace method applicable for PBS wind estimation Type de document : Article/Communication Auteurs : V.N. Sureshbabu, Auteur ; V.K. Anandan, Auteur ; Toshitaka Tsuda, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 3853 - 3861 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] décomposition spectrale [Termes IGN] écho radar [Termes IGN] filtrage du bruit [Termes IGN] image radar [Termes IGN] sous-espace [Termes IGN] vecteur propre Résumé : (Auteur) This paper mainly focuses on the advantages of subspace-based eigenvector (EV) spectral estimator to improve the power spectrum and the quality of calculations in spectrum parameter estimation. In general, the spectrum produced by most of subspace methods is sharply peaked at the frequency of complex sinusoids. Although subspace methods exhibit the advantage of spectral resolution, the retrieval of the actual spectrum width is not well observed in many cases, compared with standard Fourier estimates. Several simulation works are carried out to determine the unknown order of the signal correlation matrix, which significantly helps in obtaining the equivalent Fourier spectrum using EV along with numerous advantages of the subspace method for better estimation of spectrum parameters. Such advantages are useful in precisely obtaining the atmospheric moments (Doppler frequency, spectrum width, etc.) from the synthesized beams required for wind estimation by the postset beam steering technique. In addition, the systematic improvements done in EV are much useful for complete wind profiling up to ~ 20 km with a temporal resolution of ~ 26 s, which is reported for the first time. Numéro de notice : A2013-367 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227334 En ligne : https://doi.org/10.1109/TGRS.2012.2227334 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32505 [article]

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Leaf area index estimation of boreal and subarctic forests using VV/HH ENVISAT/ASAR data of various swaths / Terhikki Manninen in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3899 - 3909 Titre : Leaf area index estimation of boreal and subarctic forests using VV/HH ENVISAT/ASAR data of various swaths Type de document : Article/Communication Auteurs : Terhikki Manninen, Auteur Année de publication : 2013 Article en page(s) : pp 3899 - 3909 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] fauchée [Termes IGN] forêt boréale [Termes IGN] image Envisat-ASAR [Termes IGN] Leaf Area Index [Termes IGN] polarimétrie radar [Termes IGN] télédétection en hyperfréquence Résumé : (Auteur) This paper demonstrates the potential of dual polarization synthetic aperture radar (SAR) images in the estimation of the leaf area index (LAI) of boreal forests. The SAR data do not suffer from the low sun elevation and frequent cloud cover, which often complicate the use of optical wavelengths for LAI retrieval in the area. The analysis was based on a large number of environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) alternating polarization vertical polarization (VV)/horizontal polarization (HH) single-look-complex images covering several test sites, boreal and subarctic, during summers 2003-2006. The swath range from IS1 to IS7 was studied. In all test sites, a linear regression with the VV/HH backscattering ratio as the independent variable could typically be used for the estimation of LAI. All swaths could be used for the estimation, but larger incidence angles generally performed better. The best results were obtained for swath IS6. The swaths could be used also together, but better results were obtained using the diverse swaths individually. The LAI estimation error decreased essentially exponentially with the number of pixels averaged to give one backscattering value. The LAI estimation accuracy for a set of average quality ASAR images of swath IS6 reached 0.1 when the averaging number of pixels was 33 150, which would correspond to an area of about 2.2 km2 for images with no overlap. The spatial LAI estimation error decreased with the number of images covering the same area. Numéro de notice : A2013-368 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227327 En ligne : https://doi.org/10.1109/TGRS.2012.2227327 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32506 [article]

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Building a forward-mode three-dimensional reflectance model for topographic normalization of High-Resolution (1–5 m) imagery: validation phase in a forested environment / Stéphane Couturier in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3910 - 3921 Titre : Building a forward-mode three-dimensional reflectance model for topographic normalization of High-Resolution (1–5 m) imagery: validation phase in a forested environment Type de document : Article/Communication Auteurs : Stéphane Couturier, Auteur ; Jean-Philippe Gastellu-Etchegorry, Auteur ; Emmanuel Martin, Auteur ; Pavka Patino, Auteur Année de publication : 2013 Article en page(s) : pp 3910 - 3921 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] angle d'incidence [Termes IGN] classification par maximum de vraisemblance [Termes IGN] forêt tropicale [Termes IGN] image Ikonos [Termes IGN] modèle de transfert radiatif [Termes IGN] réflectance spectrale [Termes IGN] réflectance végétale Résumé : (Auteur) The aim of the topographic normalization of remotely sensed imagery (TNRSI) is to reduce reflectance variability caused by steep terrain and, subsequently, to improve land-cover classification. Recently, multiple-forward-mode (FM) (MFM) reflectance models for topographic normalizations of medium-resolution (20-30 m) satellite imagery have improved the classification of forested covers with respect to more conventional topographic corrections. We propose an FM 3-D reflectance (FM3DR) model, based on the Discrete Anisotropic Radiative Transfer simulator, for the topographic normalization of high-resolution (1-5 m) imagery. The feasibility of this approach was first verified on real IKONOS imagery for three forest types within major biomes (oak, pine, and high tropical forest) in Mexico. Next, we formalized the topographic normalization performance index and variability as relevant criteria to test TNRSI across incident angles in terms of maximum likelihood classification effectiveness. The FM3DR model outperformed five previously published topographic corrections (cosine, Minnaert, sun-canopy-sensor (SCS), Civco two-stage, and slope matching corrections), and image-based statistical strategies (Civco two-stage and slope matching corrections) tended to perform better than more analytical strategies (cosine, Minnaert, and SCS corrections). An asset of this approach versus former models is the realistic account of terrain-related variation of understory and crown cover within a cover type. On top of that, once validated across forest types, the model is sufficient for the application of a full MFM 3-D reflectance-based topographic normalization without additional field measurement. Numéro de notice : A2013-369 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2226593 En ligne : https://doi.org/10.1109/TGRS.2012.2226593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32507 [article]

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Sparse representation of GPR traces with application to signal classification / Wenbin Shao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3922 - 3930 Titre : Sparse representation of GPR traces with application to signal classification Type de document : Article/Communication Auteurs : Wenbin Shao, Auteur ; Abdesselam Bouzerdoum, Auteur ; Son Lam Phung, Auteur Année de publication : 2013 Article en page(s) : pp 3922 - 3930 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] écho radar [Termes IGN] filtre de Gabor [Termes IGN] radar pénétrant GPR Résumé : (Auteur) Sparse representation (SR) models a signal with a small number of elementary waves using an overcomplete dictionary. It has been employed for a wide range of signal and image processing applications, including denoising, deblurring, and compression. In this paper, we present an adaptive SR method for modeling and classifying ground penetrating radar (GPR) signals. The proposed method decomposes each GPR trace into elementary waves using an adaptive Gabor dictionary. The sparse decomposition is used to extract salient features for SR and classification of GPR signals. Experimental results on real-world data show that the proposed sparse decomposition achieves efficient signal representation and yields discriminative features for pattern classification. Numéro de notice : A2013-370 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2228660 En ligne : https://doi.org/10.1109/TGRS.2012.2228660 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32508 [article]

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Spectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3968 - 3981 Titre : Spectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery Type de document : Article/Communication Auteurs : Yanfeng Gu, Auteur ; Shizhe Wang, Auteur ; Xiuping Jia, Auteur Année de publication : 2013 Article en page(s) : pp 3968 - 3981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] apprentissage automatique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] espace de Hilbert [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale Résumé : (Auteur) In this paper, we address a spectral unmixing problem for hyperspectral images by introducing multiple-kernel learning (MKL) coupled with support vector machines. To effectively solve issues of spectral unmixing, an MKL method is explored to build new boundaries and distances between classes in multiple-kernel Hilbert space (MKHS). Integrating reproducing kernel Hilbert spaces (RKHSs) spanned by a series of different basis kernels in MKHS is able to provide increased power in handling general nonlinear problems than traditional single-kernel learning in RKHS. The proposed method is developed to solve multiclass unmixing problems. To validate the proposed MKL-based algorithm, both synthetic data and real hyperspectral image data were used in our experiments. The experimental results demonstrate that the proposed algorithm has a strong ability to capture interclass spectral differences and improve unmixing accuracy, compared to the state-of-the-art algorithms tested. Numéro de notice : A2013-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227757 En ligne : https://doi.org/10.1109/TGRS.2012.2227757 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32509 [article]

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Missing-area reconstruction in multispectral images under a compressive sensing perspective / Luca Lorenzi in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3998 - 4008 Titre : Missing-area reconstruction in multispectral images under a compressive sensing perspective Type de document : Article/Communication Auteurs : Luca Lorenzi, Auteur ; Farid Melgani, Auteur ; Grégoire Mercier, Auteur Année de publication : 2013 Article en page(s) : pp 3998 - 4008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par algorithme génétique [Termes IGN] équation linéaire [Termes IGN] image Formosat/COSMIC [Termes IGN] image SPOT 5 [Termes IGN] nébulosité [Termes IGN] nuage [Termes IGN] régression Résumé : (Auteur) The intent of this paper is to propose new methods for the reconstruction of areas obscured by clouds. They are based on compressive sensing (CS) theory, which allows finding sparse signal representations in underdetermined linear equation systems. In particular, two common CS solutions are adopted for our reconstruction problem: the basis pursuit and the orthogonal matching pursuit methods. A novel alternative CS solution is also proposed through a formulation within a multiobjective genetic optimization scheme. To illustrate the performances of the proposed methods, a thorough experimental analysis on FORMOsa SATellite-2 and Satellite Pour l'Observation de la Terre-5 multispectral images is reported and discussed. It includes a detailed simulation study that aims at assessing the accuracy of the methods in different qualitative and quantitative cloud-contamination conditions. Compared with state-of-the-art techniques for cloud removal, the proposed methods show a clear superiority, which makes them a promising tool in cleaning images in the presence of clouds. Numéro de notice : A2013-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227329 En ligne : https://doi.org/10.1109/TGRS.2012.2227329 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32510 [article]

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Graph-regularized low-rank representation for destriping of hyperspectral images / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4009 - 4018 Titre : Graph-regularized low-rank representation for destriping of hyperspectral images Type de document : Article/Communication Auteurs : Xiaoqiang Lu, Auteur ; Yulong Wang, Auteur ; Yuan Yuan, Auteur Année de publication : 2013 Article en page(s) : pp 4009 - 4018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] corrélation [Termes IGN] délignage [Termes IGN] image hyperspectrale Résumé : (Auteur) Hyperspectral image destriping is a challenging and promising theme in remote sensing. Striping noise is a ubiquitous phenomenon in hyperspectral imagery, which may severely degrade the visual quality. A variety of methods have been proposed to effectively alleviate the effects of the striping noise. However, most of them fail to take full advantage of the high spectral correlation between the observation subimages in distinct bands and consider the local manifold structure of the hyperspectral data space. In order to remedy this drawback, in this paper, a novel graph-regularized low-rank representation (LRR) destriping algorithm is proposed by incorporating the LRR technique. To obtain desired destriping performance, two sides of performing destriping are included: 1) To exploit the high spectral correlation between the observation subimages in distinct bands, the technique of LRR is first utilized for destriping, and 2) to preserve the intrinsic local structure of the original hyperspectral data, the graph regularizer is incorporated in the objective function. The experimental results and quantitative analysis demonstrate that the proposed method can both remove striping noise and achieve cleaner and higher contrast reconstructed results. Numéro de notice : A2013-373 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2226730 En ligne : https://doi.org/10.1109/TGRS.2012.2226730 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32511 [article]

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Semisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4032 - 4044 Titre : Semisupervised self-learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Immaculada Dopido, Auteur ; Jun Li, Auteur ; Prashanth Reddy Marpu, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4032 - 4044 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image ROSIS [Termes IGN] régression logistique Résumé : (Auteur) Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine-machine framework when compared with traditional machine-human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the use of self-learning represents an effective and promising strategy in the cont- xt of hyperspectral image classification. Numéro de notice : A2013-374 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2228275 En ligne : https://doi.org/10.1109/TGRS.2012.2228275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32512 [article]

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Deblurring and sparse unmixing for hyperspectral images / Xi-Le Zhao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4045 - 4058 Titre : Deblurring and sparse unmixing for hyperspectral images Type de document : Article/Communication Auteurs : Xi-Le Zhao, Auteur ; Fan Wang, Auteur ; Ting-Zhu Huang, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4045 - 4058 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] correction d'image [Termes IGN] flou [Termes IGN] image hyperspectrale Résumé : (Auteur) The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators for hyperspectral imaging whose point spread functions are generally system dependent and formed from axial optical aberrations in the acquisition system. An alternating direction method is developed to solve the resulting optimization problem efficiently. According to the structure of the TV regularization and sparse unmixing in the model, the convergence of the alternating direction method can be guaranteed. Experimental results are reported to demonstrate the effectiveness of the TV and sparsity model and the efficiency of the proposed numerical scheme, and the method is compared to the recent Sparse Unmixing via variable Splitting Augmented Lagrangian and TV method by Iordache et al. Numéro de notice : A2013-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227764 En ligne : https://doi.org/10.1109/TGRS.2012.2227764 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32513 [article]

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