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Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
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Titre : Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields Type de document : Article/Communication Auteurs : Junshi Xia, Auteur ; Jocelyn Chanussot, Auteur ; Peijun Du, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2532 - 2546 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse en composantes principales
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification et arbre de régression
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] performance
[Termes IGN] Rotation Forest classificationRésumé : (Auteur) In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification. Numéro de notice : A2015-519 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2361618 En ligne : https://doi.org/10.1109/TGRS.2014.2361618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77526
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2532 - 2546[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible CAESAR: an approach based on covariance matrix decomposition to improve multibaseline–multitemporal interferometric SAR processing / Gianfranco Fornaro in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
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Titre : CAESAR: an approach based on covariance matrix decomposition to improve multibaseline–multitemporal interferometric SAR processing Type de document : Article/Communication Auteurs : Gianfranco Fornaro, Auteur ; Simona Verde, Auteur ; Diego Reale, Auteur ; Antonio Pauciullo, Auteur Année de publication : 2015 Article en page(s) : pp 2050 - 2065 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse en composantes principales
[Termes IGN] décomposition d'image
[Termes IGN] image Cosmo-Skymed
[Termes IGN] image radar moirée
[Termes IGN] interferométrie différentielle
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] matrice de covariance
[Termes IGN] surveillance géologique
[Termes IGN] tomographie radarRésumé : (Auteur) Synthetic aperture radar (SAR) tomography has been strongly developed in the last years for the analysis at fine scale of data acquired by high-resolution interferometric SAR sensors as a technique alternative to classical persistent scatterer interferometry and able to resolve also multiple scatterers. SqueeSAR is a recently proposed solution which, in the context of SAR interferometry at the coarse scale analysis stage, allows taking advantage of the multilook operation to filter interferometric stacks by extracting, pixel by pixel, equivalent scattering mechanisms from the set of all available interferometric measurement collected in the data covariance matrix. In this paper, we investigate the possibilities to extend SqueeSAR by allowing the identification of multiple scattering mechanisms from the analysis of the covariance matrix. In particular, we present a new approach, named “Component extrAction and sElection SAR” algorithm, that allows taking advantage of the principal component analysis to filter interferograms relevant to the decorrelating scatterer, i.e., scatterers that may exhibit coherence losses depending on the spatial and temporal baseline distributions, and to detect and separate scattering mechanisms possibly interfering in the same pixel due to layover directly at the interferogram generation stage. The proposed module allows providing options useful for classical interferometric processing to monitor ground deformations at lower resolution (coarse scale), as well as for possibly aiding the data calibration preliminary for the subsequent full-resolution interferometric/tomographic (fine scale) analysis. Results achieved by processing high-resolution Cosmo-SkyMed data, characterized by the favorable features of a large baseline span, are presented to explain the advantages and validate this new interferometric processing solution. Numéro de notice : A2015-178 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2352853 Date de publication en ligne : 29/09/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2352853 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75897
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 2050 - 2065[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015041 RAB Revue Centre de documentation En réserve L003 Disponible Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
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Titre : Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery Type de document : Article/Communication Auteurs : Gang Chen, Auteur ; Margaret R. Metz, Auteur ; David M. Rizzo, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 38 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse en composantes principales
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] délimitation
[Termes IGN] houppier
[Termes IGN] image à ultra haute résolution
[Termes IGN] image aérienne
[Termes IGN] image MASTER
[Termes IGN] impact sur l'environnement
[Termes IGN] incendie de forêt
[Termes IGN] maladie phytosanitaire
[Termes IGN] réflectance végétaleRésumé : (auteur) Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major threats to forest health. This study examines the impacts of fire and exotic disease (sudden oak death) on forests, with an emphasis on the assessment of post-fire burn severity in a forest where trees have experienced three stages of disease progression pre-fire: early-stage (trees retaining dried foliage and fine twigs), middle-stage (trees losing fine crown fuels), and late-stage (trees falling down). The research was conducted by applying Geographic Object-Based Image Analysis (GEOBIA) to MASTER airborne images that were acquired immediately following the fire for rapid assessment and contained both high-spatial (4 m) and high-spectral (50 bands) resolutions. Although GEOBIA has gradually become a standard tool for analyzing high-spatial resolution imagery, high-spectral resolution data (dozens to hundreds of bands) can dramatically reduce computation efficiency in the process of segmentation and object-based variable extraction, leading to complicated variable selection for succeeding modeling. Hence, we also assessed two widely used band reduction algorithms, PCA (principal component analysis) and MNF (minimum noise fraction), for the delineation of image objects and the subsequent performance of burn severity models using either PCA or MNF derived variables. To increase computation efficiency, only the top 5 PCA and MNF and top 10 PCA and MNF components were evaluated, which accounted for 10% and 20% of the total number of the original 50 spectral bands, respectively. Results show that if no band reduction was applied the models developed for the three stages of disease progression had relatively similar performance, where both spectral responses and texture contributed to burn assessments. However, the application of PCA and MNF introduced much greater variation among models across the three stages. For the early-stage disease progression, neither band reduction algorithms improved or retained the accuracy of burn severity modeling (except for the use of 10 MNF components). Compared to the no-band-reduction scenario, band reduction led to a greater level of overestimation of low-degree burns and underestimation of medium-degree burns, suggesting that the spectral variation removed by PCA and MNF was vital for distinguishing between the spectral reflectance from disease-induced dried crowns (still retaining high structural complexity) and fire ash. For the middle-stage, both algorithms improved the model R2 values by 2–37%, while the late-stage models had comparable or better performance to those using the original 50 spectral bands. This could be explained by the loss of tree crowns enabling better signal penetration, thus leading to reduced spectral variation from canopies. Hence, spectral bands containing a high degree of random noise were correctly removed by the band reduction algorithms. Compared to the middle-stage, the late-stage forest stands were covered by large piles of fallen trees and branches, resulting in higher variability of MASTER imagery. The ability of band reduction to improve the model performance for these late-stage forest stands was reduced, because the valuable spectral variation representing the actual late-stage forest status was partially removed by both algorithms as noise. Our results indicate that PCA and MNF are promising for balancing computation efficiency and the performance of burn severity models in forest stands subject to the middle and late stages of sudden oak death disease progression. Compared to PCA, MNF dramatically reduced image spectral variation, generating larger image objects with less complexity of object shapes. Whereas, PCA-based models delivered superior performance in most evaluated cases suggesting that some key spectral variability contributing to the accuracy of burn severity models in diseased forests may have been removed together with true spectral noise through MNF transformations. Numéro de notice : A2015-475 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77183
in ISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 38 - 47[article]Data-driven feature learning for high resolution urban land-cover classification / Piotr Andrzej Tokarczyk (2015)
Titre : Data-driven feature learning for high resolution urban land-cover classification Type de document : Thèse/HDR Auteurs : Piotr Andrzej Tokarczyk, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2015 Collection : Dissertationen ETH num. 22544 Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted to attain the degree of doctor of sciences of ETH ZurichLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse en composantes principales
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] environnement de développement
[Termes IGN] image à très haute résolution
[Termes IGN] image à ultra haute résolution
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] prise en compte du contexte
[Termes IGN] ruissellement
[Termes IGN] surface imperméable
[Termes IGN] théorie de Dempster-ShaferRésumé : (auteur) Automated classification of aerial and satellite images is one of the fundamental challenges in remote sensing research. Over the last 30 years, researchers have tried to overcome the tedious and time consuming manual interpretation of images. With the advent of digital technologies, classification approaches facilitating image interpretation have emerged. They were quickly embraced, and nowadays classification of remote sensing imagery is a mature field with many well-established methods. However, a major yet largely unsolved problem is the design and selection of features, that would be appropriate for a specific classification task. Usually, it is not known in advance which image features would help separating object classes in an optimal way and manual feature by trial and error is still a common practice. In the last decade rapid development of remote sensing sensors gave the end-user access to very high resolution imagery. At a ground sampling distance below a meter, small objects and ne-grained texture of larger objects emerge. Thus, to properly exploit the information that these images contain, additional contextual and textural properties of objects should be extracted. Unfortunately, classification of such images is often performed using features tailored to low- and medium resolution sensors: raw pixel values, usually augmented with either simple band ratios (e.g. in form of vegetation indices), or specific texture filter banks (e.g. Gabor filters).
In this thesis, we consider the problem of feature design and selection for classification of urban land-cover from very high resolution (VHR) remote sensing images. To appropriately capture characteristic object patterns, we propose a set of simple and efficient features, called random quasi-exhaustive (RQE) feature bank. It consists of a multitude of multiscale texture features computed efficiently via integral images inside a sliding window. At the same time, we propose to sidestep manual feature selection, and let a boosting classifier choose only those features from a RQE feature bank that are able to efficiently discriminate between different object classes in a specific classification task. We believe that the proposed feature set is fairly generic to many urban remote sensing datasets, such that the features selected by the classifier can be adapted to the characteristics of a certain image: different lighting or different scene structures.
We start with presenting the developed framework for supervised classification of land-cover in urban environments. We demonstrate the efficiency of a boosting classifier used in conjunction with the RQE feature databank on five different very high resolution remote sensing datasets. Next, we move from supervised feature learning to unsupervised methods. Using random forest classifier, we investigate the performance of features extracted using data-driven methods, such as principal component analysis (PCA) or Deep Belief Networks (DBN). We show that, at least in our study, complex unsupervised and non-linear feature learning did not improve classification accuracy over standard linear baseline methods. Finally, we use the developed supervised classification framework for an application in the field of urban hydrology. We produce imperviousness maps, which are then used to model rainfall-runoff processes in urban catchments. We show that the proposed method yields results superior over state-of-the-art methods in the field of urban hydrology. Furthermore, we perform an end-to-end comparison, in which different image data sources produced using different classification methods are used as an input for a hydraulic sewer model.Numéro de notice : 17202 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Doctoral thesis : Sciences : ETH Zurich : 2015 En ligne : http://dx.doi.org/10.3929/ethz-a-010414770 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81178
Titre : Distributed dimensonality-based rendering of lidar point clouds Type de document : Article/Communication Auteurs : Mathieu Brédif , Auteur ; Bruno Vallet , Auteur ; Benjamin Ferrand, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2015 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 40-3/W3 Conférence : ISPRS 2015, Geospatial Week : Laserscanning, ISSDQ, CMRT, ISA, GeoVIS, GeoBigData 28/09/2015 03/10/2015 La Grande Motte France ISPRS OA Archives Importance : pp 559 - 564 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse en composantes principales
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] semis de points
[Termes IGN] visualisation 2DRésumé : (auteur) Mobile Mapping Systems (MMS) are now commonly acquiring lidar scans of urban environments for an increasing number of applications such as 3D reconstruction and mapping, urban planning, urban furniture monitoring, practicability assessment for persons with reduced mobility (PRM)... MMS acquisitions are usually huge enough to incur a usability bottleneck for the increasing number of non-expert user that are not trained to process and visualize these huge datasets through specific softwares. A vast majority of their current need is for a simple 2D visualization that is both legible on screen and printable on a static 2D medium, while still conveying the understanding of the 3D scene and minimizing the disturbance of the lidar acquisition geometry (such as lidar shadows). The users that motivated this research are, by law, bound to precisely georeference underground networks for which they currently have schematics with no or poor absolute georeferencing. A solution that may fit their needs is thus a 2D visualization of the MMS dataset that they could easily interpret and on which they could accurately match features with their user datasets they would like to georeference. Our main contribution is two-fold. First, we propose a 3D point cloud stylization for 2D static visualization that leverages a Principal Component Analysis (PCA)-like local geometry analysis. By skipping the usual and error-prone estimation of a ground elevation, this rendering is thus robust to non-flat areas and has no hard-to-tune parameters such as height thresholds. Second, we implemented the corresponding rendering pipeline so that it can scale up to arbitrary large datasets by leveraging the Spark framework and its Resilient Distributed Dataset (RDD) and Dataframe abstractions. Numéro de notice : C2015-004 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprsarchives-XL-3-W3-559-2015 Date de publication en ligne : 19/08/2015 En ligne : http://dx.doi.org/10.5194/isprsarchives-XL-3-W3-559-2015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80294 Subspace-based technique for speckle noise reduction in SAR images / Norashikin Yahya in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)PermalinkNovel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing / Jaime Zabalza in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)PermalinkDiscrimination des unités géologiques et structurales du socle précambrien de l'Afrique de l'ouest à l'aide de transformations multispectrales : cas du degré carré de Korhogo au nord de la Côte d'Ivoire / K. Kouamé in Photo interprétation, European journal of applied remote sensing, vol 50 n° 2 (juin 2014)PermalinkLes effets de l'oscillation Nord-Atlantique sur les transferts de masse, vus par géodésie / Pierre Valty in XYZ, n° 139 (juin - août 2014)PermalinkSpatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis / Yunzhong Shen in Journal of geodesy, vol 88 n° 1 (January 2014)PermalinkProcessing and calibration of submillimeter Fourier transform radiometer spectra from the RHUBC-II campaign / Scott N. Paine in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)PermalinkLa combinaison d'indicateurs de changement pour le suivi de l'évolution de l'occupation du sol à partir d'imagerie satellitale / Faten Katlane in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)PermalinkContribution des données ALOS et Landsat dans la cartographie et l'analyse des linéaments dans le Sahel central (Maroc occidental) / Adnane Habib in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)PermalinkAssessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy / I.D. Sanches in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)PermalinkClassification and reconstruction from random projections for hyperspectral imagery / W. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)Permalink