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Hyperspectral remote sensing image subpixel target detection based on supervised metric learning / Lefei Zhang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
[article]
Titre : Hyperspectral remote sensing image subpixel target detection based on supervised metric learning Type de document : Article/Communication Auteurs : Lefei Zhang, Auteur ; Liangpei Zhang, Auteur ; Dacheng Tao, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 4955 - 4965 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] classification dirigée
[Termes IGN] classification pixellaire
[Termes IGN] détection de cible
[Termes IGN] image hyperspectraleRésumé : (Auteur) The detection and identification of target pixels such as certain minerals and man-made objects from hyperspectral remote sensing images is of great interest for both civilian and military applications. However, due to the restriction in the spatial resolution of most airborne or satellite hyperspectral sensors, the targets often appear as subpixels in the hyperspectral image (HSI). The observed spectral feature of the desired target pixel (positive sample) is therefore a mixed signature of the reference target spectrum and the background pixels spectra (negative samples), which belong to various land cover classes. In this paper, we propose a novel supervised metric learning (SML) algorithm, which can effectively learn a distance metric for hyperspectral target detection, by which target pixels are easily detected in positive space while the background pixels are pushed into negative space as far as possible. The proposed SML algorithm first maximizes the distance between the positive and negative samples by an objective function of the supervised distance maximization. Then, by considering the variety of the background spectral features, we put a similarity propagation constraint into the SML to simultaneously link the target pixels with positive samples, as well as the background pixels with negative samples, which helps to reject false alarms in the target detection. Finally, a manifold smoothness regularization is imposed on the positive samples to preserve their local geometry in the obtained metric. Based on the public data sets of mineral detection in an Airborne Visible/Infrared Imaging Spectrometer image and fabric and vehicle detection in a Hyperspectral Mapper image, quantitative comparisons of several HSI target detection methods, as well as some state-of-the-art metric learning algorithms, were performed. All the experimental results demonstrate the effectiveness of the proposed SML algorithm for hyperspectral target detection. Numéro de notice : A2014-434 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2286195 En ligne : https://doi.org/10.1109/TGRS.2013.2286195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73971
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 4955 - 4965[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014081B RAB Revue Centre de documentation En réserve L003 Disponible Improved capability in stone pine forest mapping and management in Lebanon using hyperspectral CHTIS-Proba data relative to Landsat ETM+ / Mohamad Awad in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
[article]
Titre : Improved capability in stone pine forest mapping and management in Lebanon using hyperspectral CHTIS-Proba data relative to Landsat ETM+ Type de document : Article/Communication Auteurs : Mohamad Awad, Auteur ; Ihab Jomaa, Auteur ; Fatima Arab, Auteur Année de publication : 2014 Article en page(s) : pp. 725 - 731 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] carte topographique
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-TM
[Termes IGN] image PROBA-CHRIS
[Termes IGN] Liban
[Termes IGN] Pinus (genre)
[Termes IGN] répartition géographique
[Termes IGN] traitement automatique d'imagesRésumé : (Auteur)The Stone Pine (pinus pinea) is native to the Mediterranean region and has been used for their edible pine nuts since prehistoric times. They are widespread in horticultural cultivation as ornamental trees and planted in gardens and parks around the world. Economically speaking, the Stone Pine is very important for the agriculture sector, for tourism, and for the health sector. In this research, a pilot area located in Mount Lebanon is compared for changes in the Stone Pine cover between the years of 1962 and 2012. The comparison is based on processing a hyperspectral image provided by the European Space Agency (ESA) and a Landsat ETM+ image as well as topographic maps. Several issues related to the use of CHRiS-Proba hyperspectral images have been investigated and analyzed. The results established that hyperspectral data: (a) is 30 percent or more accurate and efficient when compared with multispectral data, and (b) helps determine precise extent of the Stone Pine cover. Numéro de notice : A2014-343 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.80.8.725 En ligne : https://doi.org/10.14358/PERS.80.8.725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73713
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp. 725 - 731[article]Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features / Z. He in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
[article]
Titre : Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features Type de document : Article/Communication Auteurs : Z. He, Auteur ; Qiang Wang, Auteur ; Y. Shen, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5150 -5163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur
[Termes IGN] décomposition en fonctions orthogonales empiriques
[Termes IGN] image hyperspectrale
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] précision de la classification
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Recently, many researchers have attempted to exploit spectral-spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral-spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral-spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples. Numéro de notice : A2014-436 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287022 En ligne : https://doi.org/10.1109/TGRS.2013.2287022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73973
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5150 -5163[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014081B RAB Revue Centre de documentation En réserve L003 Disponible vol 80 n° 8 - August 2014 - Research advances in hyperspectral remote sensing (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / American society for photogrammetry and remote sensing
[n° ou bulletin]
est un bulletin de Photogrammetric Engineering & Remote Sensing, PERS / American society for photogrammetry and remote sensing (1975 -)
Titre : vol 80 n° 8 - August 2014 - Research advances in hyperspectral remote sensing Type de document : Périodique Auteurs : American society for photogrammetry and remote sensing, Auteur Année de publication : 2014 Importance : 110 p. Langues : Anglais (eng) Descripteur : [Termes IGN] image hyperspectrale
[Termes IGN] télédétection spatialeNuméro de notice : 105-201408 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=17699 [n° ou bulletin]Contient
- Improved capability in stone pine forest mapping and management in Lebanon using hyperspectral CHTIS-Proba data relative to Landsat ETM+ / Mohamad Awad in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
- Combining hyperspectral and Lidar data for vegetation mapping in the Florida Everglades / Caiyun Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
- Hyperspectral data dimensionality reduction and the impact of multi-seasonal Hyperion EO-1 imagery on classification accuracies of tropical forest species / Manjit Saini in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
- Automated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor / Helge Aasen in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
Spectral identification of materials by reflectance spectral library search / Rama Rao Nidamanuri in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
[article]
Titre : Spectral identification of materials by reflectance spectral library search Type de document : Article/Communication Auteurs : Rama Rao Nidamanuri, Auteur ; A. M. Ramiya, Auteur Année de publication : 2014 Article en page(s) : pp 609-624 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] appariement spectral
[Termes IGN] image hyperspectrale
[Termes IGN] réflectance spectrale
[Termes IGN] signature spectraleRésumé : (auteur) Spectral library search is emerging as a viable approach for material identification and mapping by reusing spectral knowledge gained from hyperspectral remote sensing across space and time. The potential of retrieving meaningful spectral material identifications in the presence of reflectance of spectra of various material types and with various similarity metrics has been assessed in this study. Test reflectance spectra of various vegetation, minerals, soils and urban material types are identified by searching through the composite reflectance spectral library obtained by combining various institutional reflectance spectral libraries. The accuracy of material identifications under various conditions: (i) in the presence of identical, similar and dissimilar spectra; (ii) in the presence of only identical and dissimilar spectra; and (iii) in the presence of only dissimilar spectra has been assessed with several similarity metrics. Results indicate the possibility of obtaining 100% accurate material identifications by library search if the spectral library contains identical spectra. However, the presence of a large number of similar spectra, despite the presence of identical spectra, is found to increase false positives, thereby reducing the accuracy of retrievals to 82% at best. Further, the accuracy of material identifications in the presence of similar spectra is similarity metric-dependent and varied from about 52% (obtained from Binary Encoding) to 82% (obtained from Normalized Spectral Similarity Score). Overall, results support the possibility of using independent reflectance spectral libraries for material identification while calling for robust spectral similarity metrics. Numéro de notice : A2014-418 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.821175 En ligne : https://doi.org/10.1080/10106049.2013.821175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73953
in Geocarto international > vol 29 n° 5 - 6 (August - October 2014) . - pp 609-624[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Novel 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)PermalinkDecision fusion in kernel-induced spaces for hyperspectral image classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)PermalinkFeature extraction of hyperspectral images with image fusion and recursive filtering / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)PermalinkSemisupervised dual-geometric subspace projection for dimensionality reduction of hyperspectral image data / Shuyuan Yang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)PermalinkSubspace matching pursuit for sparse unmixing of hyperspectral data / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 1 (June 2014)PermalinkDouble constrained NMF for hyperspectral unmixing / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)PermalinkHyperspectral image denoising with a spatial–spectral view fusion strategy / Qiangqiang Yuan in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)PermalinkSlow feature analysis for change detection in multispectral imagery / Chen Wu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)PermalinkWetland mapping in the upper midwest United States: An object-based approach integrating Lidar and imagery radar / Lian P. Rampi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 5 (May 2014)PermalinkBayesian context-dependent learning for anomaly classification in hyperspectral imagery / Christopher Ratto in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)Permalink