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Sparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
[article]
Titre : Sparse and low-rank graph for discriminant analysis of hyperspectral imagery Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Jiabin Liu, Auteur ; Qian Du, Auteur Année de publication : 2016 Article en page(s) : pp 4094 - 4105 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] analyse en composantes principales
[Termes IGN] graphe
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] valeur propreRésumé : (Auteur) Recently, sparse graph-based discriminant analysis (SGDA) has been developed for the dimensionality reduction and classification of hyperspectral imagery. In SGDA, a graph is constructed by ℓ1-norm optimization based on available labeled samples. Different from traditional methods (e.g., k-nearest neighbor with Euclidean distance), weights in an ℓ1-graph derived via a sparse representation can automatically select more discriminative neighbors in the feature space. However, the sparsity-based graph represents each sample individually, lacking a global constraint on each specific solution. As a consequence, SGDA may be ineffective in capturing the global structures of data. To overcome this drawback, a sparse and low-rank graph-based discriminant analysis (SLGDA) is proposed. Low-rank representation has been proved to be capable of preserving global data structures, although it may result in a dense graph. In SLGDA, a more informative graph is constructed by combining both sparsity and low rankness to maintain global and local structures simultaneously. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed SLGDA significantly outperforms the state-of-the-art SGDA. Numéro de notice : A2016-879 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2536685 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2536685 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83042
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 4094 - 4105[article]Use of doppler parameters for ship velocity computation in SAR images / Alfredo Renga in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
[article]
Titre : Use of doppler parameters for ship velocity computation in SAR images Type de document : Article/Communication Auteurs : Alfredo Renga, Auteur ; Antonio Moccia, Auteur Année de publication : 2016 Article en page(s) : pp 3995 - 4011 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse discriminante
[Termes IGN] détection de cible
[Termes IGN] données Doppler
[Termes IGN] image TerraSAR-X
[Termes IGN] objet mobile
[Termes IGN] vitesse de déplacementRésumé : (Auteur) This paper deals with the exploitation of Doppler centroid measurements for ship velocity estimation from focused single-look complex synthetic aperture radar (SAR) images. An algorithm is presented, which can be used as a discrimination tool to reduce the false alarm rate of standard adaptive threshold detectors and to complement the ship detection task with velocity estimation. The outputs are the indication of the presence of a moving target and the estimate of its slant range velocity. After a review of its theoretical background, algorithm features and performance are verified by application to TerraSAR-X data. The proposed method performs robust Doppler spectrum derivation for candidate ship targets and background pixels. The presented results show that the estimated radial velocity is in very good agreement (5% root-mean-square deviation) with that resulting from the azimuth offset method. Rejection of bright sea features and azimuth ambiguities is also demonstrated, taking advantage of the performed velocity analysis. Numéro de notice : A2016-876 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2533023 En ligne : https://doi.org/10.1109/TGRS.2016.2533023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83036
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 3995 - 4011[article]Comparative analysis on utilisation of linear spectral unmixing and band ratio methods for processing ASTER data to delineate bauxite over a part of Chotonagpur plateau, Jharkhand, India / Arindam Guha in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)
[article]
Titre : Comparative analysis on utilisation of linear spectral unmixing and band ratio methods for processing ASTER data to delineate bauxite over a part of Chotonagpur plateau, Jharkhand, India Type de document : Article/Communication Auteurs : Arindam Guha, Auteur ; K. Vinod Kumar, Auteur Année de publication : 2016 Article en page(s) : pp 367 - 384 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] aluminium
[Termes IGN] analyse comparative
[Termes IGN] analyse discriminante
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] image Terra-ASTER
[Termes IGN] Inde
[Termes IGN] mineraiRésumé : (Auteur) We have attempted comparative analysis of the utility of linear spectral unmixing (LSU) method and band ratios for delineating bauxite from laterite within the lateritic bauxite provinces of Chotonagpur Plateau, Jharkhand of India. This was attempted based on processing of visible–near infrared (VNIR) and shortwave infrared (SWIR) spectral bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. In LSU method, spectral features of main constituent minerals of lateritic bauxite are used to decompose the pixel spectra to estimate the relative abundance of bauxite and laterite in each pixel to spatially delineate bauxite within laterite. We have also compared the bauxite map derived using LSU method with bauxite maps of two band ratios in terms of spatial disposition of bauxite. We also have attempted to relate the abundance values of pixels of LSU-based bauxite map with band ratio values of bauxite pixels of two selected bauxite indices. Numéro de notice : A2016-155 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047471 Date de publication en ligne : 10/06/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047471 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80395
in Geocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 367 - 384[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016021 RAB Revue Centre de documentation En réserve L003 Disponible Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
[article]
Titre : Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion Type de document : Article/Communication Auteurs : Renlong Hang, Auteur ; Qingshan Liu, Auteur ; Huihui Song, Auteur ; Yubao Sun, Auteur Année de publication : 2016 Article en page(s) : pp 783 - 794 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification multibande
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] image hyperspectrale
[Termes IGN] matriceRésumé : (Auteur) Spatial-spectral feature fusion is well acknowledged as an effective method for hyperspectral (HS) image classification. Many previous studies have been devoted to this subject. However, these methods often regard the spatial-spectral high-dimensional data as 1-D vector and then extract informative features for classification. In this paper, we propose a new HS image classification method. Specifically, matrix-based spatial-spectral feature representation is designed for each pixel to capture the local spatial contextual and the spectral information of all the bands, which can well preserve the spatial-spectral correlation. Then, matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a random sampling technique is used to produce a subspace ensemble for final HS image classification. Experiments are conducted on three HS remote sensing data sets acquired by different sensors, and experimental results demonstrate the efficiency of the proposed method. Numéro de notice : A2016-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2465899 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2465899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79996
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 783 - 794[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016021 SL Revue Centre de documentation Revues en salle Disponible Discrimination of deciduous tree species from time series of unmanned aerial system imagery / Jonathan Lisein in Plos one, vol 10 n° 11 (November 2015)
[article]
Titre : Discrimination of deciduous tree species from time series of unmanned aerial system imagery Type de document : Article/Communication Auteurs : Jonathan Lisein , Auteur ; Adrien Michez, Auteur ; Hugues Claessens, Auteur ; Philippe Lejeune, Auteur Année de publication : 2015 Article en page(s) : n° 0141006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse discriminante
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] drone
[Termes IGN] houppier
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] orthophotoplan numérique
[Termes IGN] phénologie
[Termes IGN] variation saisonnièreRésumé : (auteur) Technology advances can revolutionize Precision Forestry by providing accurate and fine forest information at tree level. This paper addresses the question of how and particularly when Unmanned Aerial System (UAS) should be used in order to efficiently discriminate deciduous tree species. The goal of this research is to determine when is the best time window to achieve an optimal species discrimination. A time series of high resolution UAS imagery was collected to cover the growing season from leaf flush to leaf fall. Full benefit was taken of the temporal resolution of UAS acquisition, one of the most promising features of small drones. The disparity in forest tree phenology is at the maximum during early spring and late autumn. But the phenology state that optimized the classification result is the one that minimizes the spectral variation within tree species groups and, at the same time, maximizes the phenologic differences between species. Sunlit tree crowns (5 deciduous species groups) were classified using a Random Forest approach for monotemporal, two-date and three-date combinations. The end of leaf flushing was the most efficient single-date time window. Multitemporal datasets definitely improve the overall classification accuracy. But single-date high resolution orthophotomosaics, acquired on optimal time-windows, result in a very good classification accuracy (overall out of bag error of 16%). Numéro de notice : A2015--031 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1371/journal.pone.0141006 En ligne : http://dx.doi.org/10.1371/journal.pone.0141006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81106
in Plos one > vol 10 n° 11 (November 2015) . - n° 0141006[article]Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA / Brian D. Bue in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)PermalinkTwo dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)PermalinkSpectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)PermalinkComplementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkAutomatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning / Qian Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkContribution of textural information from TerraSAR-X image for forest mapping / Cécile Cazals (2015)PermalinkOptimisation de la configuration d’un instrument superspectral aéroporté pour la classification : application au milieu urbain / Arnaud Le Bris (2015)PermalinkEfficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding / Junwei Han in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkHyperspectral image classification using nearest feature line embedding approach / Yang-Lang Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)PermalinkUsing hyperspectral reflectance data to assess biocontrol damage of giant salvinia / James H. Everitt in Geocarto international, vol 28 n° 5-6 (August - October 2013)Permalink