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Hyperspectral analysis of soil polluted with four types of hydrocarbons / Laura A. Reséndez-Hernández in Geocarto international, vol 34 n° 9 ([15/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 9 [15/06/2019] . - pp 925 - 942 Titre : Hyperspectral analysis of soil polluted with four types of hydrocarbons Type de document : Article/Communication Auteurs : Laura A. Reséndez-Hernández, Auteur ; Daniel Prudencio-Csapek, Auteur ; Diego Fabian Lozano Garcia, Auteur Année de publication : 2019 Article en page(s) : pp 925 - 942 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse spectrale [Termes IGN] classification Spectral angle mapper [Termes IGN] hydrocarbure [Termes IGN] multiple endmember spectral mixture analysis [Termes IGN] pétrole [Termes IGN] pollution des sols [Termes IGN] réflectance spectrale [Termes IGN] spectroradiomètre Résumé : (auteur) In this study, a high spectral resolution GER-2600 spectroradiometer was used to obtain the spectral data of soil samples that were polluted with four different types of petroleum–hydrocarbons products: Diesel, Gasoline, Crude Oil and Fuel Oil. The polluted soil samples were prepared in the laboratory at five concentrations levels: unpolluted soil, 2500, 100,000, 250,000 ppm and pure pollutant. Spectral data were pre-processed and then analysed with various approaches: Principal Components Transformation and ANOVA, Spectral Angle Mapper (SAM), Hydrocarbon Index (HI) and Spectral Mixture Analysis (SMA). The results showed that it was possible to determine the different spectral response between clean soil and some of the polluted soils: crude oil at concentrations higher than 100,000 ppm were the easiest to recognize; while samples polluted with gasoline at concentrations below 250,000 ppm were the most difficult to distinguish from non-polluted samples. Numéro de notice : A2019-516 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1451921 date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2018.1451921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93870 [article]

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Discrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans / Tanumi Kumar in Geocarto international, vol 34 n° 4 ([15/03/2019])  

Public [article]  inGeocarto international > vol 34 n° 4 [15/03/2019] . - pp 415 - 442 Titre : Discrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans Type de document : Article/Communication Auteurs : Tanumi Kumar, Auteur ; Abhishek Mandal, Auteur ; Dibyendu Dutta, Auteur ; R. Nagaraja, Auteur ; Vinay Kumar Dadhwal, Auteur Année de publication : 2019 Article en page(s) : pp 415 - 442 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse discriminante [Termes IGN] Avicennia marina [Termes IGN] classification barycentrique [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification Spectral angle mapper [Termes IGN] image EO1-Hyperion [Termes IGN] Inde [Termes IGN] indice de végétation [Termes IGN] mangrove [Termes IGN] palétuvier Résumé : (Auteur) In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels. Numéro de notice : A2019-451 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1408699 date de publication en ligne : 11/12/2017 En ligne : https://doi.org/10.1080/10106049.2017.1408699 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92839 [article]

Evaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)  

Public [article]  inApplied geomatics > vol 9 n° 1 (March 2017) . - pp 1 - 12 Titre : Evaluation of pan-sharpening methods for spatial and spectral quality Type de document : Article/Communication Auteurs : Jagalingam Pushparaj, Auteur ; Arkal Vittal Hegde, Auteur Année de publication : 2017 Article en page(s) : pp 1 - 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de Gram-Schmidt [Termes IGN] analyse en composantes principales [Termes IGN] classification Spectral angle mapper [Termes IGN] évaluation [Termes IGN] filtre passe-haut [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] image Quickbird [Termes IGN] ondelette [Termes IGN] pansharpening (fusion d'images) [Termes IGN] qualité géométrique (image) [Termes IGN] qualité radiométrique (image) [Termes IGN] transformation de Brovey [Termes IGN] transformation intensité-teinte-saturation Résumé : (auteur) Many pan-sharpening methods have been proposed to fuse the high spectral and low spatial resolution of multispectral (MS) image with the high spatial resolution of panchromatic (PAN) image to produce a multispectral image with improved spatial resolution. In this study, the effectiveness of pan-sharpening methods such as principal component analysis (PCA), brovey transform (BT), modified intensity hue saturation (M-IHS), multiplicative, wavelet-intensity-hue-saturation (W-IHS), wavelet principal component analysis (W-PCA), hyperspectral colour space (HCS), high-pass filter (HPF), gram-schmidt (GS), subtractive resolution merge (SRM), Fuze Go and Ehlers was assessed and compared by fusing the PAN and MS imagery of Quickbird-2. The qualities of the pan-sharpening methods were evaluated by both visual and quantitative analyses with respect to spatial and spectral fidelity. In quantitative analysis, the spectral indices such as spectral angle mapper (SAM), relative dimensionless global error in synthesis (ERGAS), structural similarity index method (SSIM), relative average spectral error (RASE), correlation coefficient (CC) and universal image quality index (Q) were used. The spatial indices such as spatial correlation coefficient (SCC), gradient and image entropy (E) were used. The result of both analyses revealed that the Ehlers and Fuze Go methods performed better than the other methods. The Ehlers method was superior by retaining the colour information, and Fuze Go best enhanced the spatial details in the fused image. Numéro de notice : A2017-357 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-016-0179-2 date de publication en ligne : 13/12/2016 En ligne : http://doi.org/10.1007/s12518-016-0179-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85763 [article]

Spectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery / L. Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 9 (September 2014) . - pp 849 - 861 Titre : Spectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery Type de document : Article/Communication Auteurs : L. Yan, Auteur ; X. Niu, Auteur Année de publication : 2014 Article en page(s) : pp 849 - 861 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] angle d'incidence [Termes IGN] classification Laplacian Eigenmap [Termes IGN] classification Spectral angle mapper [Termes IGN] détection de cible [Termes IGN] distance euclidienne [Termes IGN] image hyperspectrale [Termes IGN] réduction [Termes IGN] réflectance spectrale [Termes IGN] végétation Résumé : In traditional manifold learning of hyperspectral imagery, distances among pixels are defined in terms of Euclidean distance, which is not necessarilly the best choice because of its sensitivity to variations in spectrum magnitudes. Selecting Laplacian Eignemaps (LE) as the test method, this paper studies the effects of distance metric selection in LE and proposes a spectral-angle-based LE method (LE-SA)to be compared against the traditional LE-based on Euclidean distance (LE-ED). Le-SA and LA-ED were applied to two airborne hyperspectral data sets and the dimensionlity-reduced data were quantitatively evalueted. Experimental results demonstrated that LE-SA is able to suppress the variations within the same type of features, such as variations in vegetation and those in illuminations due to shade orientations, and maintain a higher level of overall separability among different features than LE-ED. Further, the potential usage of a single LA-SA or LE-ED band for target detection is discussed. Numéro de notice : A2014-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.9.849 En ligne : https://doi.org/10.14358/PERS.80.9.849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74888 [article]

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Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey / Ugur Alganci in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 1053 - 1065 Titre : Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey Type de document : Article/Communication Auteurs : Ugur Alganci, Auteur ; Elif Sertel, Auteur ; Mutlu Ozdogan, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 1053 - 1065 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte agricole [Termes IGN] classification orientée objet [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification Spectral angle mapper [Termes IGN] cultures [Termes IGN] image Landsat-TM [Termes IGN] image SPOT 5 [Termes IGN] occupation du sol [Termes IGN] parcelle agricole [Termes IGN] photo-interprétation assistée par ordinateur [Termes IGN] Turquie Résumé : (Auteur) This research investigates the accuracy of pixel- and object-based classification techniques across varying spatial resolutions to identify crop types at parcel level and estimate the area at six test sites to find the optimum data source for the identification of crop parcels. Multi-sensor data with spatial resolutions of 2.5 m, 5 m and 10 m from SPOT5 and 30 m from Landsat-5 TM were used. Maximum Likelihood (ML), Spectral Angle Mapper (SAM), and Support Vector Machines (SVM) were used as pixel-based methods in addition to object-based image classification (OBC). Post-classification methods were applied to the output of pixel-based classification to minimize the noise effects and heterogeneity within the agricultural parcels. In addition, processing-time performance of the algorithms was evaluated for the test sites and district scale classification. OBC results provided comparatively the best performance for both parcel identification and area estimation at 10 m and finer spatial resolution levels. SVM followed OBC at 2.5 m and 5 m resolutions but accuracies decreased dramatically with coarser resolutions. ML and SAM results were worse up to 30 m resolution for both crop type identification and area estimation. In general, parcel identification efficiency was strongly correlated with spatial resolution while the classification algorithm was a more effective factor than spatial resolution for area estimation accuracy. Results also provided an opportunity to discuss the effects of image resolution and the classification algorithm independent factors such as parcel size, spatial distribution of crop types and crop patterns. Numéro de notice : A2013-599 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.1053 En ligne : https://doi.org/10.14358/PERS.79.11.1053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32735 [article]

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La télédétection au service des études urbaines : expansion de la ville de Pondichéry entre 1973 et 2009 / Emilien Kieffer in Géomatique expert, n° 95 (01/11/2013)  

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Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region / George P. Petropoulos in Geocarto international, vol 28 n° 1-2 (February - May 2013)  

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Applying six classifiers to airborne hyperspectral imagery for detecting giant reed / C. Yang in Geocarto international, vol 27 n° 5 (August 2012)  

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Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors / R.J. Murphy in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)  

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Traitements numériques des images de télédétection, Vol. 3. Traitements appliqués à la photo-interprétation / Olivier de Joinville (2012)

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Cartographie des sols hydromorphes de la région des lacs (Côte d'Ivoire) par l'approche du spectral angle mapper (SAM) / G. Zro Bi in Revue Française de Photogrammétrie et de Télédétection, n° 195 (Novembre 2011)

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A comparison of error metrics and constraints for multiple endmember spectral analysis and spectral angle mapper / P.E. Dennison in Remote sensing of environment, vol 93 n° 3 (15/11/2004)

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