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Shoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method / Audrey Minghelli in Remote sensing, vol 12 n° 16 (August-2 2020)  

Public [article]  inRemote sensing > vol 12 n° 16 (August-2 2020) . - n° 2664 Titre : Shoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method Type de document : Article/Communication Auteurs : Audrey Minghelli, Auteur ; Jérôme Spagnoli, Auteur ; Manchun Lei , Auteur ; Malik Chami, Auteur ; Sabine Charmasson, Auteur Année de publication : 2020 Projets : AMORAD / Radakovitch, Olivier Article en page(s) : n° 2664 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] baie [Termes IGN] classification multibande [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de contours [Termes IGN] écume [Termes IGN] image Worldview [Termes IGN] littoral [Termes IGN] Sendaï [Termes IGN] trait de côte Résumé : (auteur) Foam is often present in satellite images of coastal areas and can lead to serious errors in the detection of shorelines especially when processing high spatial resolution images ( Numéro de notice : A2020-570 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12162664 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.3390/rs12162664 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95877 [article]

A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1568 -1586 Titre : A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data Type de document : Article/Communication Auteurs : Qingwang Wang, Auteur ; Yanfeng Gu, Auteur Année de publication : 2020 Article en page(s) : pp 1568 -1586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Amérique du nord [Termes IGN] analyse discriminante [Termes IGN] calcul tensoriel [Termes IGN] carte d'occupation du sol [Termes IGN] classification multibande [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] état de l'art [Termes IGN] extraction de traits caractéristiques [Termes IGN] image multibande [Termes IGN] modèle géométrique [Termes IGN] semis de points [Termes IGN] tenseur [Termes IGN] vectorisation [Termes IGN] voisinage (relation topologique) Résumé : (Auteur) Multispectral light detection and ranging (MS-LiDAR) systems open the door to the possibility in the 3-D land cover classification at a finer scale using only point cloud data. This article proposes a model based on the tensor representation for multispectral point cloud classification. The proposed method combines the 3-D local spatial structure of each multispectral point by characterizing the point with a second-order tensor. The first mode of the tensor indicates the spatial location and spectral information of each point (i.e., the row of the second-order tensor) and the second mode denotes the neighborhood geometric and spectral structures (i.e., the column of the second-order tensor). Then we develop a novel tensor manifold discriminant embedding (TMDE) algorithm to extract the geometric–spectral features for multispectral point clouds classification. TMDE solves the mapping matrices of each mode by preserving the intraclass samples’ distribution further making it more compact and maximizing the distance of different classes. Finally, the support vector machine classifier with the extracted features as input is used to implement the classification of multispectral point clouds. Experiments are conducted on two real multispectral point cloud data sets. The experimental results demonstrate that the proposed method can achieve significant improvements in classification accuracies in comparison with several state-of-the-art algorithms. Numéro de notice : A2020-086 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947081 Date de publication en ligne : 30/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947081 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94660 [article]

Geographic Information Systems in Geospatial Intelligence, ch. 5. Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering / Arnaud Le Bris (2019)  

Public Titre de série : Geographic Information Systems in Geospatial Intelligence, ch. 5 Titre : Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering Type de document : Chapitre/Contribution Auteurs : Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur ; Xavier Briottet , Auteur ; Nicolas Paparoditis , Auteur Editeur : London [UK] : IntechOpen Année de publication : 2019 Projets : 1-Pas de projet / Radakovitch, Olivier Importance : 4 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] carte d'occupation du sol [Termes IGN] classification multibande [Termes IGN] image hyperspectrale [Termes IGN] optimisation (mathématiques) Résumé : (auteur) Hyperspectral imagery consists of hundreds of contiguous spectral bands. However, most of them are redundant. Thus a subset of well-chosen bands is generally sufficient for a specific problem, enabling to design adapted superspectral sensors dedicated to specific land cover classification. Related both to feature selection and extraction, spectral optimization identifies the most relevant band subset for specific applications, involving a band subset relevance score as well as a method to optimize it. This study first focuses on the choice of such relevance score. Several criteria are compared through both quantitative and qualitative analyses. To have a fair comparison, all tested criteria are compared to classic hyperspectral data sets using the same optimization heuristics: an incremental one to assess the impact of the number of selected bands and a stochastic one to obtain several possible good band subsets and to derive band importance measures out of intermediate good band subsets. Last, a specific approach is proposed to cope with the optimization of bandwidth. It consists in building a hierarchy of groups of adjacent bands, according to a score to decide which adjacent bands must be merged, before band selection is performed at the different levels of this hierarchy. Numéro de notice : H2019-008 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.5772/intechopen.88507 Date de publication en ligne : 20/12/2019 En ligne : http://dx.doi.org/10.5772/intechopen.88507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95734

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 783 - 794 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] matrice Résumé : (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 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2016021	SL	Revue	Centre de documentation	Revues en salle	Disponible

Hyperspectral Band Selection by Multitask Sparsity Pursuit / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 631 -644 Titre : Hyperspectral Band Selection by Multitask Sparsity Pursuit Type de document : Article/Communication Auteurs : Yuan Yuan, Auteur ; Qi Wang, Auteur Année de publication : 2015 Article en page(s) : pp 631 -644 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage dirigé [Termes IGN] bande spectrale [Termes IGN] classification multibande [Termes IGN] couleur à l'écran [Termes IGN] image hyperspectrale [Termes IGN] visualisation de données Résumé : (Auteur) Hyperspectral images have been proved to be effective for a wide range of applications; however, the large volume and redundant information also bring a lot of inconvenience at the same time. To cope with this problem, hyperspectral band selection is a pertinent technique, which takes advantage of removing redundant components without compromising the original contents from the raw image cubes. Because of its usefulness, hyperspectral band selection has been successfully applied to many practical applications of hyperspectral remote sensing, such as land cover map generation and color visualization. This paper focuses on groupwise band selection and proposes a new framework, including the following contributions: 1) a smart yet intrinsic descriptor for efficient band representation; 2) an evolutionary strategy to handle the high computational burden associated with groupwise-selection-based methods; and 3) a novel MTSP-based criterion to evaluate the performance of each candidate band combination. To verify the superiority of the proposed framework, experiments have been conducted on both hyperspectral classification and color visualization. Experimental results on three real-world hyperspectral images demonstrate that the proposed framework can lead to a significant advancement in these two applications compared with other competitors. Numéro de notice : A2015-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2326655 En ligne : https://doi.org/10.1109/TGRS.2014.2326655 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75621 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Open source geospatial tools / Daniel McInerney (2015)

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Utilisation d'images à haute et très haute résolution pour la mise à jour de la carte de l'Aquila (Italie) / Valerio Baiocchi in Géomatique expert, n° 85 (01/03/2012)

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Multiple Spectral–Spatial Classification Approach for Hyperspectral Data / Yuliya Tarabalka in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)  

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Nouvelle approche du réseau ARTMAP flou : application à la classification multi-spectrale des images SPOT XS de la baie d'Alger / F. Alilat in Revue Française de Photogrammétrie et de Télédétection, n° 177 (Juin 2005)

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Télédétection aérospatiale / Jules Wilmet (1996)

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Euroconference SIG / Euroconference SIG (1994)

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Technical papers 1992 ASPRS - ACSM annual convention : ASPRS 58th annual convention, ACSM 52nd annual convention, Volume 1. ASPRS Technical papers / L. Gleasner (1992)

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Technical papers 1992 ASPRS - ACSM annual convention : ASPRS 58th annual convention, ACSM 52nd annual convention, Volume 2. ACSM Technical papers / L.J. Urban (1992)

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An assessment of some factors influencing multispectral land-cover classification / G. Peng in Photogrammetric Engineering & Remote Sensing, PERS, vol 56 n° 6 (june 1990)  

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An enhanced classification approach to change detection in semi-arid environments / P.G. Pilon in Photogrammetric Engineering & Remote Sensing, PERS, vol 54 n° 12 (december 1988)  

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