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Three-dimensional deformation monitoring of urban infrastructure by tomographic SAR using multitrack TerraSAR-X data stacks / Sina Montazeri in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6868 - 6878 Titre : Three-dimensional deformation monitoring of urban infrastructure by tomographic SAR using multitrack TerraSAR-X data stacks Type de document : Article/Communication Auteurs : Sina Montazeri, Auteur ; Xiao Xiang Zhu, Auteur ; Michael Eineder, Auteur ; Richard Bamler, Auteur Année de publication : 2016 Article en page(s) : pp 6868 - 6878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse comparative [Termes IGN] Berlin [Termes IGN] déformation d'édifice [Termes IGN] image radar moirée [Termes IGN] image TerraSAR-X [Termes IGN] semis de points [Termes IGN] surveillance d'ouvrage [Termes IGN] tomographie radar Résumé : (Auteur) Differential synthetic aperture radar tomography (D-TomoSAR), similar to its conventional counterparts such as differential interferometric SAR and persistent scatterer interferometry, is only capable of capturing 1-D deformation along the satellite's line of sight. In this paper, we propose a method based on L1-norm minimization within local spatial cubes to reconstruct 3-D displacement vectors from TomoSAR point clouds available from at least three different viewing geometries. The methodology is applied on two pairs of cross-heading-combination of ascending and descending-TerraSAR-X (TS-X) spotlight image stacks over the city of Berlin. The linear deformation rate and the amplitude of seasonal deformation are decomposed, and the results from two test sites with remarkable deformation pattern are discussed in detail. The results, to our knowledge, demonstrate the first attempt for motion decomposition using TomoSAR data from multiple viewing geometries. Numéro de notice : A2016-919 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2585741 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2585741 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83322 [article]

Adaptive estimation of the stable boundary layer height using combined Lidar and microwave radiometer observations / Umar Saeed in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6895 - 6906 Titre : Adaptive estimation of the stable boundary layer height using combined Lidar and microwave radiometer observations Type de document : Article/Communication Auteurs : Umar Saeed, Auteur ; Francesc Rocadenbosch, Auteur ; Susanne Crewell, Auteur Année de publication : 2016 Article en page(s) : pp 6895 - 6906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse de données [Termes IGN] analyse diachronique [Termes IGN] données lidar [Termes IGN] radiomètre à hyperfréquence [Termes IGN] Satellite Microwave Radiometer Résumé : (Auteur) A synergetic approach for the estimation of stable boundary layer height (SBLH) using lidar and microwave radiometer (MWR) data is presented. Vertical variance of the backscatter signal from a ceilometer is used as an indicator of the aerosol stratification in the nocturnal stable boundary layer. This hypothesis is supported by a statistical analysis over one month of observations. Thermodynamic information from the MWR-derived potential temperature is incorporated as coarse estimate of the SBLH. Data from the two instruments are adaptively assimilated by using an extended Kalman filter (EKF). A first test of the algorithm is performed by applying it to collocated Vaisala CT25K ceilometer and humidity and temperature profiler MWR data collected during the HD(CP)2 Observational Prototype Experiment (HOPE) campaign at Jülich, Germany. The application of the algorithm to different atmospheric scenarios reveals the superior performance of the EKF compared to a nonlinear least squares estimator, particularly in nonidealized conditions. Numéro de notice : A2016-920 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2586298 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2586298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83324 [article]

Urban damage level mapping based on scattering mechanism investigation using fully polarimetric SAR Data for the 3.11 East Japan earthquake / Si-Wei Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6919 - 6929 Titre : Urban damage level mapping based on scattering mechanism investigation using fully polarimetric SAR Data for the 3.11 East Japan earthquake Type de document : Article/Communication Auteurs : Si-Wei Chen, Auteur ; Xue-Song Wang, Auteur ; Motoyuki Sato, Auteur Année de publication : 2016 Article en page(s) : pp 6919 - 6929 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] carte synthétique [Termes IGN] décomposition [Termes IGN] diffusion du rayonnement [Termes IGN] dommage matériel [Termes IGN] Japon [Termes IGN] polarimétrie radar [Termes IGN] zone urbaine Résumé : (Auteur) A quick response to a large-scale natural disaster such as earthquake and tsunami is vital to mitigate further loss. Remote sensing, especially the spaceborne sensors, provides the possibility to monitor a very large scale area in a short time and with regular revisit circle. Damage ranges and damage levels of the destructed urban areas are extremely important information for rescue planning after an event. Rapid mapping of the urban damage levels with synthetic aperture radar (SAR) is still challenging. Compared with single-polarization SAR, fully polarimetric SAR (PolSAR) has a better potential to understand the urban damage from the viewpoint of scattering mechanism investigation. In radar polarimetry, the dominant double-bounce scattering mechanism in an urban area is primarily induced by the ground-wall structures and can reflect the changes of these structures. In this sense, urban damage level in terms of destroyed ground-wall structures can be indicated by the reduction of the dominant double-bounce scattering mechanism, which is the basis of this study. This work first establishes and validates the linear relationship between the urban damage level and the proposed polarimetric damage index using polarimetric model-based decomposition. Then, efforts are focused on the development of a rapid urban damage level mapping technique which mainly includes two steps of urban area extraction and polarimetric damage level estimation. The 3.11 East Japan earthquake and tsunami inducing great-scale destruction are adopted for study using L-band multitemporal spaceborne PolSAR data. Experimental studies demonstrate that the estimated damage levels are closely consistent to the ground-truth. The final urban damage level map for the full scene is generated thereafter. Results achieved in this study further validate the necessity of exploring fully polarimetric technique for damage assessment. Numéro de notice : A2016-921 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2588325 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2588325 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83325 [article]

Hyperspectral feature extraction using total variation component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6976 - 6985 Titre : Hyperspectral feature extraction using total variation component analysis Type de document : Article/Communication Auteurs : Behnood Rasti, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2016 Article en page(s) : pp 6976 - 6985 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] extraction automatique [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) In this paper, a novel feature extraction method, called orthogonal total variation component analysis (OTVCA), is proposed for remotely sensed hyperspectral data. The features are extracted by minimizing a total variation (TV) penalized optimization problem. The TV penalty promotes piecewise smoothness of the extracted features which is useful for classification. A cyclic descent algorithm called OTVCA-CD is proposed for solving the minimization problem. In the experiments, OTVCA is applied on a rural hyperspectral image having low spatial resolution and an urban hyperspectral image having high spatial resolution. The features extracted by OTVCA show considerable improvements in terms of classification accuracy compared with features extracted by other state-of-the-art methods. Numéro de notice : A2016-922 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2593463 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2593463 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83326 [article]

Automatic parameter selection for intensity-based registration of imagery to LiDAR data / Ebadat Ghanbari Parmehr in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7032 - 7043 Titre : Automatic parameter selection for intensity-based registration of imagery to LiDAR data Type de document : Article/Communication Auteurs : Ebadat Ghanbari Parmehr, Auteur ; Clive Simpson Fraser, Auteur ; Chunsun Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 7032 - 7043 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] appariement d'images [Termes IGN] appariement de données localisées [Termes IGN] densité de probabilité [Termes IGN] données lidar [Termes IGN] image aérienne [Termes IGN] image binaire [Termes IGN] segmentation binaire [Termes IGN] semis de points Résumé : (Auteur) Automatic registration of multisensor data, for example, imagery and Light Detection And Ranging (LiDAR), is a basic step in data fusion in the field of geospatial information processing. Mutual information (MI) has recently attracted research attention as a statistical similarity measure for intensity-based registration of multisensor images in the related fields of computer vision and remote sensing. Since MI-based registration methods rely on joint probability density functions (pdfs) for the data sets, errors in pdf estimation can affect the MI value, causing registration failure due to the presence of nonmonotonic surfaces of similarity measure. The quality of the estimated pdf is highly dependent upon both the bin size and the smoothing technique used in the pdf estimation procedure. The lack of a general approach to assign an appropriate bin size value for the pdf of multisensor data reduces both the level of automation and the robustness of the registration. In this paper, a novel bin size selection approach is proposed to improve registration reliability. The proposed method determines the best (uniform or variable) bin size for the pdf estimation via an analysis of the relationship between the similarity measure values of the data and the adopted geometric transformation. This highlights the role of the component of MI sensitive to the transformation, rather than the MI component that is unrelated to the transformation, such as noise. The performance of the proposed method for the registration of aerial imagery to LiDAR point clouds is investigated, and experimental results are compared with those achieved through a feature-based registration method. Numéro de notice : A2016-923 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2594294 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2594294 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83327 [article]

An inquiry on contrast enhancement methods for satellite images / Jose-Luis Lisani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7044 - 7054 Titre : An inquiry on contrast enhancement methods for satellite images Type de document : Article/Communication Auteurs : Jose-Luis Lisani, Auteur ; Julien Michel, Auteur ; Jean-Michel Morel, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7044 - 7054 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] amélioration du contraste [Termes IGN] couleur à l'écran [Termes IGN] détection d'ombre [Termes IGN] intensité lumineuse Résumé : (Auteur) Enhancement algorithms are absolutely necessary for the visualization of both shadowed and bright image regions. Defining algorithms that permit to visualize them simultaneously without altering the image content is therefore extremely relevant for remote sensing applications. In this paper, we present the results of two successive benchmarks which tested the performance of the state-of-the-art contrast enhancement and benchmarks algorithms applied to satellite images. Experts from the French Space Agency Centre National d'Etudes Spatiales (CNES), Service Régional de Traitement d'Image et de Télédétection (SERTIT), and two European universities assessed the quality and fidelity of the results of several state-of-the-art enhancement algorithms on the excerpts from seven images (five Pleiades and two simulated 30-cm images). The first benchmark permitted to tighten the procedure and the selection of the test images for the second one, and to make a first selection of concurrent algorithms. The second benchmark not only included the best algorithms selected by the first benchmark but also added even more competitors in the tone-mapping class. The results of both benchmarks were coherent. They point a particular retinex-based algorithm as the best compromise between the competitive requirements of a contrast enhancement in dark regions and a preservation of detail in bright parts. Numéro de notice : A2016-924 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2594339 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2594339 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83328 [article]

Hierarchical and adaptive phase correlation for precise disparity estimation of UAV images / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7092 - 7104 Titre : Hierarchical and adaptive phase correlation for precise disparity estimation of UAV images Type de document : Article/Communication Auteurs : Jie Li, Auteur ; Yiguang Liu, Auteur ; Shuangli Du, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7092 - 7104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] drone [Termes IGN] image aérienne [Termes IGN] modèle numérique de surface [Termes IGN] stéréoscopie Résumé : (Auteur) When using fixed-window phase correlation (PC) to estimate the disparity of stereo images, the precision is usually rather poor due to large depth differences of scenes and noise, and this problem is specially severe when using unmanned aerial vehicle (UAV) image pairs to extract the digital elevation model of mountain land. To tackle this problem, this paper proposes a hierarchical and adaptive PC, which includes three steps: First, PC with the initialized window is performed to coarsely estimate a disparity value, along with the peak of the Dirichlet function for each pixel; then, an additional round of PC is performed for each pixel using the window of smaller size and with being guided by the coarsely estimated disparity; finally, the previous two steps are iteratively performed until convergence. In particular, using the peak of the Dirichlet function of each pixel in step two, we can drop out the influence of dramatically changing areas such as river; moreover, the scheme can minimize the influence of boundary overreach. The novel scheme has been tested on a large number of UAV images captured at mountainous regions in southwest China, showing that the proposed method is superior to the state-of-the-art methods, especially in handling UAV images of the high mountains and rivers. Numéro de notice : A2016-925 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2595861 En ligne : https://doi.org/10.1109/TGRS.2016.2595861 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83331 [article]

An integrated framework for the spatio–temporal–spectral fusion of remote sensing images / Huanfeng Shen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7135 - 7148 Titre : An integrated framework for the spatio–temporal–spectral fusion of remote sensing images Type de document : Article/Communication Auteurs : Huanfeng Shen, Auteur ; Xiangchao Meng, Auteur ; Liangpei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 7135 - 7148 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de fusion [Termes IGN] données spatiotemporelles [Termes IGN] fusion d'images [Termes IGN] fusion de données multisource [Termes IGN] image spectrale Résumé : (Auteur) Remote sensing satellite sensors feature a tradeoff between the spatial, temporal, and spectral resolutions. In this paper, we propose an integrated framework for the spatio-temporal-spectral fusion of remote sensing images. There are two main advantages of the proposed integrated fusion framework: it can accomplish different kinds of fusion tasks, such as multiview spatial fusion, spatio-spectral fusion, and spatio-temporal fusion, based on a single unified model, and it can achieve the integrated fusion of multisource observations to obtain high spatio-temporal-spectral resolution images, without limitations on the number of remote sensing sensors. The proposed integrated fusion framework was comprehensively tested and verified in a variety of image fusion experiments. In the experiments, a number of different remote sensing satellites were utilized, including IKONOS, the Enhanced Thematic Mapper Plus (ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Hyperspectral Digital Imagery Collection Experiment (HYDICE), and Système Pour l' Observation de la Terre-5 (SPOT-5). The experimental results confirm the effectiveness of the proposed method. Numéro de notice : A2016-926 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2596290 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2596290 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83332 [article]

Imaging the internal structure of an alpine glacier via L-band airborne SAR tomography / Stefano Tebaldini in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7197 - 7209 Titre : Imaging the internal structure of an alpine glacier via L-band airborne SAR tomography Type de document : Article/Communication Auteurs : Stefano Tebaldini, Auteur ; Thomas Nagler, Auteur ; Helmut Rott, Auteur ; Achim Heilig, Auteur Année de publication : 2016 Article en page(s) : pp 7197 - 7209 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Alpes [Termes IGN] Autriche [Termes IGN] bande L [Termes IGN] données localisées 3D [Termes IGN] image aérienne [Termes IGN] image radar moirée [Termes IGN] radar pénétrant GPR [Termes IGN] tomographie radar Résumé : (Auteur) In this paper, we report results from the analysis of 3-D L-band airborne synthetic aperture radar (SAR) acquisitions acquired in March 2014 over the Mittelbergferner glacier, Austrian Alps, during the European Space Agency (ESA) campaign AlpTomoSAR. The campaign included coincident in situ measurements of snow and ice properties and ground-penetrating radar (GPR) data acquired at 600 and 200 MHz over a total length of 18 km. Radar data were acquired by repeatedly flying an L-band SAR along an oval racetrack at an altitude of about 1300 m over the glacier, such that two data stacks from opposite views are obtained. Data from all passes were coherently combined to achieve 3-D resolution capabilities, resulting in the generation of 3-D tomographic SAR (TomoSAR) cubes, where each voxel represents L-band radar reflectivity from a particular location in the 3-D space at a spatial resolution on the order of meters. TomoSAR cubes were finally corrected to account for wave propagation velocity into the ice, which was a necessary step to associate the observed features with their geometrical location, hence enabling a direct comparison to GPR data. The TomoSAR cubes show the complexity of the glacier subsurface scattering. Most areas are characterized by surface scattering in proximity of the ice surface, plus a complex pattern of in-depth volumetric scattering beneath and scattering at the ice/bedrock interface. Various subsurface features observed in GPR transects at 200 MHz clearly showed up in TomoSAR sections as well, particularly firn bodies, crevasses, layer transitions, and bedrock reflection down to 50 m below the ice surface. Numéro de notice : A2016-927 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2597361 En ligne : https://doi.org/10.1109/TGRS.2016.2597361 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83341 [article]

An iterative interpolation deconvolution algorithm for superresolution land cover mapping / Feng Ling in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7210 - 7222 Titre : An iterative interpolation deconvolution algorithm for superresolution land cover mapping Type de document : Article/Communication Auteurs : Feng Ling, Auteur ; Giles M. Foody, Auteur ; Yong Ge, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7210 - 7222 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification du maximum a posteriori [Termes IGN] déconvolution [Termes IGN] image à ultra haute résolution [Termes IGN] itération Résumé : (Auteur) Superresolution mapping (SRM) is a method to produce a fine-spatial-resolution land cover map from coarse-spatial-resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation and then determines class labels of fine-resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between the observed coarse-resolution fraction images and the latent fine-resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms and should be replaced by deconvolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation deconvolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse-resolution fraction images with an area-to-area interpolation algorithm and produces an initial fine-resolution land cover map by deconvolution. The fine-spatial-resolution land cover map is then updated by reconvolution, back-projection, and deconvolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multispectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors and can preserve the patch continuity and the patch boundary smoothness simultaneously. Moreover, the IID algorithm produced fine-resolution land cover maps with higher accuracies than those produced by other SRM algorithms. Numéro de notice : A2016-928 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598534 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2598534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83342 [article]

Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing / Lei Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7223 - 7235 Titre : Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing Type de document : Article/Communication Auteurs : Lei Zhang, Auteur ; Wei Wei, Auteur ; Yanning Zhang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7223 - 7235 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] bruit blanc [Termes IGN] compression d'image [Termes IGN] image hyperspectrale [Termes IGN] reconstruction d'image Résumé : (Auteur) The ability to accurately represent a hyperspectral image (HSI) as a combination of a small number of elements from an appropriate dictionary underpins much of the recent progress in hyperspectral compressive sensing (HCS). Preserving structure in the sparse representation is critical to achieving an accurate reconstruction but has thus far only been partially exploited because existing methods assume a predefined dictionary. To address this problem, a structured sparsity-based hyperspectral blind compressive sensing method is presented in this study. For the reconstructed HSI, a data-adaptive dictionary is learned directly from its noisy measurements, which promotes the underlying structured sparsity and obviously improves reconstruction accuracy. Specifically, a fully structured dictionary prior is first proposed to jointly depict the structure in each dictionary atom as well as the correlation between atoms, where the magnitude of each atom is also regularized. Then, a reweighted Laplace prior is employed to model the structured sparsity in the representation of the HSI. Based on these two priors, a unified optimization framework is proposed to learn both the dictionary and sparse representation from the measurements by alternatively optimizing two separate latent variable Bayes models. With the learned dictionary, the structured sparsity of HSIs can be well described by the reweighted Laplace prior. In addition, both the learned dictionary and sparse representation are robust to noise corruption in the measurements. Extensive experiments on three hyperspectral data sets demonstrate that the proposed method outperforms several state-of-the-art HCS methods in terms of the reconstruction accuracy achieved. Numéro de notice : A2016-929 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598577 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2598577 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83343 [article]

Multiband image fusion based on spectral unmixing / Qi Wei in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7236 - 7249 Titre : Multiband image fusion based on spectral unmixing Type de document : Article/Communication Auteurs : Qi Wei, Auteur ; José Bioucas-Dias, Auteur ; Nicolas Dobigeon, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7236 - 7249 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] fusion d'images [Termes IGN] image à basse résolution [Termes IGN] image à haute résolution [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] matrice de covariance Résumé : (Auteur) This paper presents a multiband image fusion algorithm based on unsupervised spectral unmixing for combining a high-spatial-low-spectral-resolution image and a low-spatial-high-spectral-resolution image. The widely used linear observation model (with additive Gaussian noise) is combined with the linear spectral mixture model to form the likelihoods of the observations. The nonnegativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem. The joint fusion and unmixing problem is then formulated as maximizing the joint posterior distribution with respect to the endmember signatures and abundance maps. This optimization problem is attacked with an alternating optimization strategy. The two resulting subproblems are convex and are solved efficiently using the alternating direction method of multipliers. Experiments are conducted for both synthetic and semi-real data. Simulation results show that the proposed unmixing-based fusion scheme improves both the abundance and endmember estimation compared with the state-of-the-art joint fusion and unmixing algorithms. Numéro de notice : A2016-930 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598784 En ligne : https://doi.org/10.1109/TGRS.2016.2598784 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83344 [article]

Discriminative-dictionary-learning-based multilevel point-cluster features for ALS point-cloud classification / Zhenxin Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7309 - 7322 Titre : Discriminative-dictionary-learning-based multilevel point-cluster features for ALS point-cloud classification Type de document : Article/Communication Auteurs : Zhenxin Zhang, Auteur ; Liqiang Zhang, Auteur ; Xiaohua Tong, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7309 - 7322 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage automatique [Termes IGN] classificateur [Termes IGN] codage [Termes IGN] extraction de points [Termes IGN] problème de Dirichlet [Termes IGN] semis de points Résumé : (Auteur) Efficient presentation and recognition of on-ground objects from airborne laser scanning (ALS) point clouds are a challenging task. In this paper, we propose an approach that combines a discriminative-dictionary-learning-based sparse coding and latent Dirichlet allocation (LDA) to generate multilevel point-cluster features for ALS point-cloud classification. Our method takes advantage of the labels of training data and each dictionary item to enforce discriminability in sparse coding during the dictionary learning process and more accurately further represent point-cluster features. The multipath AdaBoost classifiers with the hierarchical point-cluster features are trained, and we apply them to the classification of unknown points by the heritance of the recognition results under different paths. Experiments are performed on different ALS point clouds; the experimental results have shown that the extracted point-cluster features combined with the multipath classifiers can significantly enhance the classification accuracy, and they have demonstrated the superior performance of our method over other techniques in point-cloud classification. Numéro de notice : A2016-931 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2599163 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2599163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83345 [article]

Class-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7351 - 7365 Titre : Class-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification Type de document : Article/Communication Auteurs : Tianzhu Liu, Auteur ; Yanfeng Gu, Auteur ; Xiuping Jia, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7351 - 7365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] exploration de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] méthode fondée sur le noyau Résumé : (Auteur) In recent years, many studies on hyperspectral image classification have shown that using multiple features can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. This paper proposes a class-specific sparse MKL (CS-SMKL) framework to improve the capability of hyperspectral image classification. In terms of the features, extended multiattribute profiles are adopted because it can effectively represent the spatial and spectral information of hyperspectral images. CS-SMKL classifies the hyperspectral images, simultaneously learns class-specific significant features, and selects class-specific weights. Using an L1-norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for the classification of any two classes. More precisely, our CS-SMKL determines the associated weights of optimal base kernels for any two classes and results in improved classification performances. The advantage of the proposed method is that only the features useful for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on three hyperspectral data sets. The experimental results show that the proposed method achieves better performances for hyperspectral image classification compared with several state-of-the-art algorithms, and the results confirm the capability of the method in selecting the useful features. Numéro de notice : A2016-932 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2600522 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2600522 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83346 [article]