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Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-machine technique / B.C. Gao in Remote sensing of environment, vol 90 n° 4 (30/04/2004)
[article]
Titre : Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-machine technique Type de document : Article/Communication Auteurs : B.C. Gao, Auteur ; M.J. Montes, Auteur ; C.O. Davis, Auteur Année de publication : 2004 Article en page(s) : pp 424 - 433 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] capteur multibande
[Termes IGN] étalonnage radiométrique
[Termes IGN] étalonnage spectral
[Termes IGN] image hyperspectrale
[Termes IGN] longueur d'onde
[Termes IGN] spectre électromagnétique
[Termes IGN] spectromètre imageurRésumé : (Auteur) The concept of imaging spectrometry, or hyperspectral imaging, is becoming increasingly popular in scientific communities in recent years. Hyperspectral imaging data covering the spectral region between 0.4 and 2.5 um and collected from aircraft and satellite platforms have been used in the study of the earth's atmosphere, land surface, and ocean color properties, as well as on planetary missions. In order to make such quantitative studies, accurate radiometric and spectral calibrations of hyperspectral imaging data are necessary. Calibration coefficients for all detectors in an imaging spectrometer obtained in a laboratory may need to be adjusted when applied to data obtained from an aircraft or a satellite platform. Shifts in channel center wavelengths and changes in spectral resolution may occur when an instrument is airborne or spaceborne due to vibrations, and to changes in instrument temperature and pressure. In this paper, we describe an algorithm for refining spectral calibrations of imaging spectrometer data using observed features in the data itself. The algorithm is based on spectrum-matching of atmospheric water vapor, oxygen, and carbon dioxide bands, and solar Fraunhofer lines. It has been applied to real data sets acquired with airborne and spaceborne imaging spectrometers. Numéro de notice : A2004-189 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.09.002 En ligne : https://doi.org/10.1016/j.rse.2003.09.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26716
in Remote sensing of environment > vol 90 n° 4 (30/04/2004) . - pp 424 - 433[article]Classification of remotely sensed imagery stochastic gradient boosting as a refinement of classification tree analysis / R. Lawrence in Remote sensing of environment, vol 90 n° 3 (15/04/2004)
[article]
Titre : Classification of remotely sensed imagery stochastic gradient boosting as a refinement of classification tree analysis Type de document : Article/Communication Auteurs : R. Lawrence, Auteur ; A. Bunn, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 331 - 336 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification
[Termes IGN] décomposition d'image
[Termes IGN] image hyperspectrale
[Termes IGN] image Ikonos
[Termes IGN] image Landsat-ETM+
[Termes IGN] image PROBE
[Termes IGN] précision de la classification
[Termes IGN] sylvicultureRésumé : (Auteur) Classification tree analysis (CTA) provides an effective suite of algorithms for classifying remotely sensed data, but it has the limitations of (1) not searching for optimal tree structures and (2) being adversely affected by outliers, inaccurate training data, and unbalanced data sets. Stochastic gradient boosting (SGB) is a refinement of standard CTA that attempts to minimize these limitations by (1) using classification errors to iteratively refine the trees using a random sample of the training data and (2) combining the multiple trees iteratively developed to classify the data. We compared traditional CTA results to SGB for three remote sensing based data sets, an IKONOS image from the Sierra Nevada Mountains of California, a Probe-1 hyperspectral image from the Virginia City mining district of Montana, and a series of Landsat ETM+ images from the Greater Yellowstone Ecosystem (GYE). SGB improved the overall accuracy of the IKONOS classification from 84% to 95% and the Probe-1 classification from 83% to 93%. The worst performing classes using CTA exhibited the largest increases in class accuracy using SGB. A slight decrease in overall classification accuracy resulted from the SGB analysis of the Landsat data. Numéro de notice : A2004-200 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.01.007 En ligne : https://doi.org/10.1016/j.rse.2004.01.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26727
in Remote sensing of environment > vol 90 n° 3 (15/04/2004) . - pp 331 - 336[article]Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modelling and validation in the context of precision agriculture / D. Haboudane in Remote sensing of environment, vol 90 n° 3 (15/04/2004)
[article]
Titre : Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modelling and validation in the context of precision agriculture Type de document : Article/Communication Auteurs : D. Haboudane, Auteur ; J.R. Miller, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 337 - 352 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture de précision
[Termes IGN] analyse comparative
[Termes IGN] blé (céréale)
[Termes IGN] chlorophylle
[Termes IGN] cultures
[Termes IGN] données de terrain
[Termes IGN] Glycine max
[Termes IGN] Green Leaf Area Index
[Termes IGN] image CASI
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] maïs (céréale)
[Termes IGN] modèle de transfert radiatif
[Termes IGN] prévision
[Termes IGN] réflectance végétaleRésumé : (Auteur) A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVII, MCARII, MTV12, and MCAR12) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTV12) and a modified chlorophyll absorption ratio index (MCAR12), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCAR12 and MTV12 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r) being 0.98 for soybean, 0.89 for com, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively. Numéro de notice : A2004-201 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.12.013 En ligne : https://doi.org/10.1016/j.rse.2003.12.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26728
in Remote sensing of environment > vol 90 n° 3 (15/04/2004) . - pp 337 - 352[article]Linear mixture analysis-based compression for hyperspectral image analysis / Q. Du in IEEE Transactions on geoscience and remote sensing, vol 42 n° 4 (April 2004)
[article]
Titre : Linear mixture analysis-based compression for hyperspectral image analysis Type de document : Article/Communication Auteurs : Q. Du, Auteur ; C.I. Chang, Auteur Année de publication : 2004 Article en page(s) : pp 875 - 891 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image numérique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] compression de données
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectraleRésumé : (Auteur) Due to significantly improved spectral resolution produced by hyperspectral sensors, the hand-to-hand correlation is generally very high and can be removed without loss of crucial information. Data compression is an effective means to eliminate such redundancy resulting from high interband correlation. In hyperspectral imagery, various information comes from different signal sources, which include man-made targets, natural backgrounds, unknown clutters, interferers, unidentified anomalies, etc. In many applications, whether or not a compression technique is effective is measured by the degree of information loss rather than information recovery. For example, compression of noise or interferers is highly desirable to image analysis and interpretation. In this paper, we present an unsupervised fully constrained least squares (UFCLS) linear spectral mixture analysis (LSMA)-based compression technique for hyperspectral target detection and classification. Unlike most compression techniques, which deal directly with grayscale images, the proposed compression approach generates and encodes the fractional abundance images of targets of interest present in an image scene to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in these fractional abundance images, the loss of information may have little impact on image analysis. On some occasions, it even improves performance analysis. Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data are used for experiments to evaluate our proposed LSMA-based compression technique used for applications in hyperspectral detection and image classification. The classification results using the original data and the UFCLS-decompressed data are shown to be very close with no visible difference. But a compression ratio for the HYDICE data with water bands removed can achieve as high as 138: 1 with peak SNR (PSNR) 33 dB, while a compression ratio of the AVIRIS scene also with water bands removed is 90: 1 with PSNR 40 dB. Numéro de notice : A2004-187 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.816668 En ligne : https://doi.org/10.1109/TGRS.2003.816668 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26714
in IEEE Transactions on geoscience and remote sensing > vol 42 n° 4 (April 2004) . - pp 875 - 891[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-04041 RAB Revue Centre de documentation En réserve L003 Disponible Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa / Onisimo Mutanga in Remote sensing of environment, vol 90 n° 1 (15/03/2004)
[article]
Titre : Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa Type de document : Article/Communication Auteurs : Onisimo Mutanga, Auteur ; Andrew K. Skidmore, Auteur Année de publication : 2004 Article en page(s) : pp 104 - 115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] analyse comparative
[Termes IGN] azote
[Termes IGN] bande infrarouge
[Termes IGN] bande visible
[Termes IGN] erreur moyenne quadratique
[Termes IGN] herbe
[Termes IGN] image aérienne
[Termes IGN] image HYMAP
[Termes IGN] image hyperspectrale
[Termes IGN] régression linéaire
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificiel
[Termes IGN] savaneRésumé : (Auteur) A new integrated approach, involving continuum-removed absorption features, the red edge position and neural networks, is developed and applied to map grass nitrogen concentration in an African savanna rangeland. Nitrogen, which largely determines the nutritional quality of grasslands, is commonly the most limiting nutrient for grazers. Therefore, the remote sensing of foliar nitrogen concentration in savanna rangelands is important for an improved understanding of the distribution and feeding patterns of wildlife. Continuum removal was applied on two absorption features located in the visible (R 550-757) and the SWIR (R 2015-2199) from an atmospherically corrected HYMAP MK1 image. A feature selection algorithm was used to select wavelength variables from the absorption features. Selected band depths from the absorption features as well as the red edge position (REP) were input into a backpropagation neural network. The best-trained neural network was used to map nitrogen concentration over the whole study area. Results indicate that the new integrated approach could explain 60% of the variation in savanna grass nitrogen concentration on an independent test data set, with a root mean square error (rmse) of 0. 13 (+ 8.3 0% of the mean observed nitrogen concentration). This result is better compared to the result obtained using multiple linear regression, which yielded an R of 38%, with a RMSE of 0.16 (+ 10.30% of the mean observed nitrogen concentration) on an independent test data set. The study demonstrates the potential of airborne hyperspectral data and neural networks to estimate and ultimately to map nitrogen concentration in the mixed species environments of Southern Africa. Numéro de notice : A2004-130 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.12.004 En ligne : https://doi.org/10.1016/j.rse.2003.12.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26657
in Remote sensing of environment > vol 90 n° 1 (15/03/2004) . - pp 104 - 115[article]La télédétection avec ENVI / Françoise de Blomac in SIG la lettre, n° 55 (mars 2004)PermalinkA wavelet approach to road extraction from high spatial resolution remotely-sensed imagery / Qiaoping Zhang in Geomatica, vol 58 n° 1 (March 2004)PermalinkEstimating fragmentation effects on simulated forest net primary productivity derived from satellite imagery / Nicholas C. Coops in International Journal of Remote Sensing IJRS, vol 25 n° 4 (February 2004)PermalinkPredicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features / Onisimo Mutanga in Remote sensing of environment, vol 89 n° 3 (15/02/2004)PermalinkMapping coal fires based on OMIS1 thermal infrared band image / Y. Wan in International Journal of Remote Sensing IJRS, vol 25 n° 3 (February 2004)PermalinkFrom mobile mapping to telegeoinformatics: paradigm shift in geospatial data acquisition, processing, and management / Dorota A. Grejner-Brzezinska in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 2 (February 2004)PermalinkUnsupervised classification of hyperspectral data: an ICA mixture model based approach / Chintan A. Shah in International Journal of Remote Sensing IJRS, vol 25 n° 2 (January 2004)PermalinkHyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data / N. Oppelt in International Journal of Remote Sensing IJRS, vol 25 n° 1 (January 2004)PermalinkSpectral characteristics and feature selection of hyperspectral remote sensing data / X. Jiang in International Journal of Remote Sensing IJRS, vol 25 n° 1 (January 2004)PermalinkAutomated subpixel photobathymetry and water quality mapping / R.L. Huguenin in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 1 (January 2004)Permalink