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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)  

Public [article]  inRemote sensing of environment > vol 93 n° 3 (15/11/2004) . - pp 359 - 367 Titre : A comparison of error metrics and constraints for multiple endmember spectral analysis and spectral angle mapper Type de document : Article/Communication Auteurs : P.E. Dennison, Auteur ; K. Halligan, Auteur ; D.A. Roberts, Auteur Année de publication : 2004 Article en page(s) : pp 359 - 367 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] albedo [Termes IGN] analyse comparative [Termes IGN] analyse de mélange spectral d’extrémités multiples [Termes IGN] appariement spectral [Termes IGN] calcul d'erreur [Termes IGN] classe d'objets [Termes IGN] classification Spectral angle mapper [Termes IGN] erreur moyenne quadratique [Termes IGN] métrique Résumé : (Auteur) Spectral matching algorithms can be used for the identification of unknown spectra based on a measure of similarity with one or more known spectra. Two popular spectral matching algorithms use different error metrics and constraints to determine the existence of a spectral match. Multiple endmember spectral mixture analysis (MESMA) is a linear mixing model that uses a root mean square error (RMSE) error metric. Spectral angle mapper (SAM) compares two spectra using a spectral angle error metric. This paper compares two endmember MESMA and SAM using a spectral library containing six land cover classes. RMSE and spectral angle for models within each land cover class were directly compared. The dependence of RMSE on the albedo of the modeled spectrum was also explored. RMSE and spectral angle were found to be closely related, although not equivalent, due to variations in the albedo of the modeled spectra. Error constraints applied to both models resulted in large differences in the number of spectral matches. Using MESMA, the number of spectra modeled within the error constraint increased as the albedo of the modeled spectra decreased. The value of the error constraint used was shown to make a much larger difference in the number of spectra modeled than the choice of spectral matching algorithm. Numéro de notice : A2004-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.07.013 En ligne : https://doi.org/10.1016/j.rse.2004.07.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26960 [article]

Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape / M.L. Clarke in Remote sensing of environment, vol 91 n° 1 (15/05/2004)  

Public [article]  inRemote sensing of environment > vol 91 n° 1 (15/05/2004) . - pp 68 - 89 Titre : Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape Type de document : Article/Communication Auteurs : M.L. Clarke, Auteur ; D. Clark, Auteur ; D.A. Roberts, Auteur Année de publication : 2004 Article en page(s) : pp 68 - 89 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] corrélation linéaire [Termes IGN] données lidar [Termes IGN] erreur moyenne quadratique [Termes IGN] forêt tropicale [Termes IGN] interpolation inversement proportionnelle à la distance [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] sous-bois [Termes IGN] télémétrie laser aéroporté Résumé : (Auteur) Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m. A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 in tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r 2 = 0,97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods. Numéro de notice : A2004-237 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.02.008 En ligne : https://doi.org/10.1016/j.rse.2004.02.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26764 [article]

Measuring the physical composition of urban morphology using multiple endmember spectral mixture models / T. Rashed in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 9 (September 2003) . - pp 1011 - 1020 Titre : Measuring the physical composition of urban morphology using multiple endmember spectral mixture models Type de document : Article/Communication Auteurs : T. Rashed, Auteur ; J.R. Weeks, Auteur ; J. Rogan, Auteur ; R.W. Powell, Auteur ; D.A. Roberts, Auteur Année de publication : 2003 Article en page(s) : pp 1011 - 1020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de mélange spectral d’extrémités multiples [Termes IGN] erreur moyenne quadratique [Termes IGN] image Landsat-TM [Termes IGN] Los Angeles [Termes IGN] méthode robuste [Termes IGN] morphologie mathématique [Termes IGN] pixel Numéro de notice : A2003-231 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.69.9.1011 En ligne : https://doi.org/10.14358/PERS.69.9.1011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22526 [article]

Spectral resolution requirements for mapping urban areas / Martin Herold in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1907 - 1919 Titre : Spectral resolution requirements for mapping urban areas Type de document : Article/Communication Auteurs : Martin Herold, Auteur ; M.E. Gardner, Auteur ; D.A. Roberts, Auteur Année de publication : 2003 Article en page(s) : pp 1907 - 1919 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] cartographie urbaine [Termes IGN] classification [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image Ikonos [Termes IGN] image Landsat-TM [Termes IGN] image multibande [Termes IGN] image optique [Termes IGN] milieu urbain [Termes IGN] occupation du sol [Termes IGN] précision de la classification Résumé : (Auteur) This study evaluated how spectral resolution of spatial resolution optical remote sensing data influences detailed mapping of urban land cover. A comprehensive regional spectral library and low altitude data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were used to characterize the spectral properties of urban land cover. The Bhattacharyya distance was applied as a measure of spectral separability to determine a most suitable subset of 14 AVIRIS bands for urban mapping. We evaluated the performance of this spectral setting versus common multispectral sensors such as Ikonos by assessing classification accuracy for 26 urban land cover classes. Significant limitations for current multispectral sensors were identified, where the location and broadband character of the spectral bands only marginally resolved the complex spectral characteristics of the urban environment, especially for built surface types. However, the AVIRIS classification accuracy did not exceed 66.6% for 22 urban cover types, primarily due to spectral similarities of specific urban materials and high withinclass variability. Numéro de notice : A2003-248 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.815238 En ligne : https://doi.org/10.1109/TGRS.2003.815238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22543 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery / C.C. Funk in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1410 - 1420 Titre : Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery Type de document : Article/Communication Auteurs : C.C. Funk, Auteur ; J. Theiler, Auteur ; C.C. Borel, Auteur ; D.A. Roberts, Auteur Année de publication : 2001 Article en page(s) : pp 1410 - 1420 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] analyse spectrale [Termes IGN] image hyperspectrale [Termes IGN] image thermique Résumé : (Auteur) The use of matched filters on hyperspectral data has made it possible to detect faint signatures. This study uses a modified k-means clustering to improve matched filter performance. Several simple bivariate cases are examined in detail, and the interaction of filtering and partitioning is discussed. The authors show that clustering can reduce within-class variance and group pixels with similar correlation structures. Both of these features improve filter performance. The traditional k-means algorithm is modified to work with a sample of the image at each iteration and is tested against two hyperspectral datasets. A new “extreme” centroid initialization technique is introduced and shown to speed convergence. Several matched filtering formulations (the simple matched filter, the clutter matched filter, and the saturated matched filter) are compared for a variety of number of classes and synthetic hyperspectral images. The performance of the various clutter matched filter formulations is similar, all are about an order of magnitude better than the simple matched filter. Clustering is found to improve the performance of all matched filter formulations by a factor of two to five. Clustering in conjunction with clutter matched filtering can improve fifty-fold over the simple case, enabling very weak signals to be detected in hyperspectral images. Numéro de notice : A2001-200 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934073 En ligne : https://doi.org/10.1109/36.934073 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21894 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-01071	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spectral mixture analysis of simulated thermal infrared spectrometry data: an initial temperature estimate bounded TESSMA search approach / E.F. Collins in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)  

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