Centre de documentation
scientifique

A discriminative metric learning based anomaly detection method / Bo Du in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 6844 - 6857 Titre : A discriminative metric learning based anomaly detection method Type de document : Article/Communication Auteurs : Bo Du, Auteur ; L. Zhang, Auteur Année de publication : 2014 Article en page(s) : pp 6844 - 6857 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage (cognition) [Termes IGN] détection d'anomalie [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution Résumé : (Auteur) Due to the high spectral resolution, anomaly detection from hyperspectral images provides a new way to locate potential targets in a scene, especially those targets that are spectrally different from the majority of the data set. Conventional Mahalanobis-distance-based anomaly detection methods depend on the background statistics to construct the anomaly detection metric. One of the main problems with these methods is that the Gaussian distribution assumption of the background may not be reasonable. Furthermore, these methods are also susceptible to contamination of the conventional background covariance matrix by anomaly pixels. This paper proposes a new anomaly detection method by effectively exploiting a robust anomaly degree metric for increasing the separability between anomaly pixels and other background pixels, using discriminative information. First, the manifold feature is used so as to divide the pixels into the potential anomaly part and the potential background part. This procedure is called discriminative information learning. A metric learning method is then performed to obtain the robust anomaly degree measurements. Experiments with three hyperspectral data sets reveal that the proposed method outperforms other current anomaly detection methods. The sensitivity of the method to several important parameters is also investigated. Numéro de notice : A2014-541 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2303895 En ligne : https://doi.org/10.1109/TGRS.2014.2303895 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74158 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014111A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning / X. Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 7086 - 7098 Titre : Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning Type de document : Article/Communication Auteurs : X. Li, Auteur ; H. Shen, Auteur ; L. Zhang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 7086 - 7098 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage (cognition) [Termes IGN] détection d'ombre [Termes IGN] réflectance [Termes IGN] température de surface Résumé : (Auteur) With regard to quantitative remote sensing products in the visible and infrared ranges, thick clouds and accompanying shadows are an inevitable source of noise. Due to the absence of adequate supporting information from the data themselves, it is a formidable challenge to accurately restore the surficial information underlying large-scale clouds. In this paper, dictionary learning is expanded into the multitemporal recovery of quantitative data contaminated by thick clouds and shadows. This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts. In order to make better use of the temporal correlations, the expanded KSVD algorithm seeks an optimized temporal path, and the expanded Bayesian method adaptively weights the temporal correlations. In the experiments, the proposed algorithms are applied to a reflectance product and a land surface temperature product, and the respective advantages of the two algorithms are investigated. The results show that, from both the qualitative visual effect and the quantitative objective evaluation, the proposed methods are effective. Numéro de notice : A2014-543 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2307354 En ligne : https://doi.org/10.1109/TGRS.2014.2307354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74160 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014111A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A structure-aware global optimization method for reconstructing 3-D tree models from terrestrial laser scanning data / Z. Wang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 2 (September 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 9 Tome 2 (September 2014) . - pp 5653 - 5669 Titre : A structure-aware global optimization method for reconstructing 3-D tree models from terrestrial laser scanning data Type de document : Article/Communication Auteurs : Z. Wang, Auteur ; L. Zhang, Auteur ; Tian Fang, Auteur Année de publication : 2014 Article en page(s) : pp 5653 - 5669 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] optimisation (mathématiques) [Termes IGN] reconstruction 3D Résumé : (Auteur) A 3-D tree structure plays an important role in many scientific fields, including forestry and agriculture. For example, terrestrial laser scanning (TLS) can efficiently capture high-precision 3-D spatial arrangements and structure of trees as a point cloud. In the past, several methods to reconstruct 3-D trees from the TLS point cloud were proposed. However, in general, they fail to process incomplete TLS data. To address such incomplete TLS data sets, a new method that is based on a structure-aware global optimization approach (SAGO) is proposed. The SAGO first obtains the approximate tree skeleton from a distance minimum spanning tree (DMst) and then defines the stretching directions of the branches on the tree skeleton. Based on these stretching directions, the SAGO recovers missing data in the incomplete TLS point cloud. The DMst is applied again to obtain the refined tree skeleton from the optimized data, and the tree skeleton is smoothed by employing a Laplacian function. To reconstruct 3-D tree models, the radius of each branch section is estimated, and leaves are added to form the crown geometry. The developed methodology has been extensively evaluated by employing a dozen TLS point clouds of various types of trees. Both qualitative and quantitative performance evaluation results have indicated that the SAGO is capable of effectively reconstructing 3-D tree models from grossly incomplete TLS point clouds with significant amounts of missing data. Numéro de notice : A2014-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2291815 En ligne : https://ieeexplore.ieee.org/document/6693725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74171 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014091B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Perception-based shape retrieval for 3D building models / M. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 75 (January 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 75 (January 2013) . - pp 76 - 91 Titre : Perception-based shape retrieval for 3D building models Type de document : Article/Communication Auteurs : M. Zhang, Auteur ; L. Zhang, Auteur ; P. Mathiolpoulos, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 76 - 91 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] appariement de formes [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] requête spatiale Résumé : (Auteur) With the help of 3D search engines, a large number of 3D building models can be retrieved freely online. A serious disadvantage of most rotation-insensitive shape descriptors is their inability to distinguish between two 3D building models which are different at their main axes, but appear similar when one of them is rotated. To resolve this problem, we present a novel upright-based normalization method which not only correctly rotates such building models, but also greatly simplifies and accelerates the abstraction and the matching of building models’ shape descriptors. Moreover, the abundance of architectural styles significantly hinders the effective shape retrieval of building models. Our research has shown that buildings with different designs are not well distinguished by the widely recognized shape descriptors for general 3D models. Motivated by this observation and to further improve the shape retrieval quality, a new building matching method is introduced and analyzed based on concepts found in the field of perception theory and the well-known Light Field descriptor. The resulting normalized building models are first classified using the qualitative shape descriptors of Shell and Unevenness which outline integral geometrical and topological information. These models are then put in on orderly fashion with the help of an improved quantitative shape descriptor which we will term as Horizontal Light Field Descriptor, since it assembles detailed shape characteristics. To accurately evaluate the proposed methodology, an enlarged building shape database which extends previous well-known shape benchmarks was implemented as well as a model retrieval system supporting inputs from 2D sketches and 3D models. Various experimental performance evaluation results have shown that, as compared to previous methods, retrievals employing the proposed matching methodology are faster and more consistent with human recognition of spatial objects. In addition these performance evaluation results have verified that the proposed methodology does not sacrifice the matching accuracy while significantly improves the efficiency when matching 3D building models. Numéro de notice : A2013-034 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.10.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.10.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32172 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automatic generation of 2.5D terrain models without occluding routes of interest / Hao Deng in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 11 (November 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 11 (November 2012) . - pp 1175 - 1185 Titre : Automatic generation of 2.5D terrain models without occluding routes of interest Type de document : Article/Communication Auteurs : Hao Deng, Auteur ; L. Zhang, Auteur ; J. Ma, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 1175 - 1185 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données localisées 2,5D [Termes IGN] modèle numérique de terrain [Termes IGN] montagne [Termes IGN] réseau routier [Termes IGN] visibilité Résumé : (Auteur) When a car drives in mountainous regions, the views based on conventional perspective projection often suffer from features of interest being occluded. We propose a method for generating disocclusion views in mountainous regions. The terrain is segmented to build a potential set of occluders; and then an optimized viewpoint is determined, and elevations are rearranged. To obtain a smooth deformed terrain, a smooth displacement function is introduced to deform the level-of-detail terrain models. Compared with previous methods, the merit of this study lies in automatically generating disocclusion views with temporal coherence while keeping the details of the deformed terrain the same as the original terrain. Experiments performed on the 4098 pixel x 4098 pixel mountainous terrain landscape prove that the disocclusion views can achieve 42 to 58 frames/second. Moreover, the shapes of the features of interest on the driving route without occlusion and the spatial configuration of geographical landmarks in its neighborhood can be easily recognized. Numéro de notice : A2012-585 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.11.1175 En ligne : https://doi.org/10.14358/PERS.78.11.1175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32031 [article]

Monitoring ground subsidence in Shanghai maglev area using two kinds of SAR data / J. Wu in Journal of applied geodesy, vol 6 n° 3-4 (November 2012)  

Permalink

Hyperspectral image denoising employing a spectral-spatial adaptive total variation model / Q. Yuan in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 1 (October 2012)  

Permalink

A variational gradient-based fusion method for visible and SWIR imagery / H. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 9 (September 2012)  

Permalink

A geometry and texture coupled flexible generalization of urban building models / M. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)  

Permalink

Satellite SAR geocoding with refined RPC model / L. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)  

Permalink

A multispectral and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery / X. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 7 (July 2011)  

Permalink

An automatic mosaicking method for building facade texture mapping using a monocular close-range image sequence / Z. Kang in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 3 (May - June 2010)  

Permalink

Spatiotemporal analysis of rural-urban land conversion / B. Huang in International journal of geographical information science IJGIS, vol 23 n°3-4 (march - april 2009)  

Permalink

Classification of very high spatial resolution imagery based on the fusion of edge and multispectral information / X. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 12 (December 2008)  

Permalink

Texture feature fusion with neighborhood oscillating tabu search for high resolution image classification / L. Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 3 (March 2008)  

Permalink