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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)
[article]
Titre : La télédétection avec ENVI Type de document : Article/Communication Auteurs : Françoise de Blomac, Auteur Année de publication : 2004 Article en page(s) : pp 10 - 11 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] cartographie 3D
[Termes IGN] ENVI
[Termes IGN] fonctionnalité
[Termes IGN] image hyperspectrale
[Termes IGN] image numériqueRésumé : (Auteur) ENVI, système d'analyse d'images proposé par RSI France fait peu de bruit. Développé entre autres par la NASA, ce logiciel est utilisé dans le monde entier par des clients prestigieux, dans des chaînes de production stratégiques. D'une grande richesse fonctionnelle, adapté à de multiples capteurs, ENVI sait aussi se mettre au service d'utilisateurs plus modestes en leur proposant une boîte à outils particulièrement complète et structurée. C'est sans doute une des raisons de son succès dans les milieux universitaires. Numéro de notice : A2004-107 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26634
in SIG la lettre > n° 55 (mars 2004) . - pp 10 - 11[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 286-04031 RAB Revue Centre de documentation En réserve L003 Disponible Predicting 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)
[article]
Titre : Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features Type de document : Article/Communication Auteurs : Onisimo Mutanga, Auteur ; Andrew K. Skidmore, Auteur ; Herbert H.T. Prins, Auteur Année de publication : 2004 Article en page(s) : pp 393 - 408 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Afrique du sud (état)
[Termes IGN] azote
[Termes IGN] biochimie
[Termes IGN] carbone
[Termes IGN] forêt tropicale
[Termes IGN] image hyperspectrale
[Termes IGN] parc naturel national
[Termes IGN] paturage
[Termes IGN] phosphore
[Termes IGN] potassium
[Termes IGN] prairie
[Termes IGN] réflectance végétale
[Termes IGN] régression linéaire
[Termes IGN] savane
[Termes IGN] spectroradiomètre
[Termes IGN] zone intertropicaleRésumé : (Auteur) The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better understanding of wildlife and livestock feeding patterns. Although remote sensing techniques have proved useful for assessing the concentration of foliar biochemicals under controlled laboratory conditions, more investigation is required to assess their capabilities in the field where inconsistent results have been obtained so far. We investigated the possibility of determining the concentration of in situ biochemicals in a savanna rangeland, using the spectral reflectance of five grass species. Canopy spectral measurements were taken in the field using a GER 3700 spectroradiometer. We tested the utility of using four variables derived from continuum-removed absorption features for predicting canopy nitrogen, phosphorous, potassium, calcium and magnesium concentration: (i) continuum-removed derivative reflectance (CRDR), (ii) band depth (BD), (iii) band depth ratio (BDR) and (iv) normalised band depth index (NBDI). Stepwise linear regression was used to select wavelengths from the absorption-feature-based variables. Univariate correlation analysis was also done between the first derivative reflectance and biochemicals. Using a training data set, the variables derived from continuum-removed absorption features could predict biochemicals with R2 values ranging from 0.43 to 0.80. Results were highest using CRDR data, which yielded R2 values of 0.70, 0.80, 0.64, 0.50 and 0.68 with root mean square errors (RMSE) of 0.01, 0.004, 0.03, 0.01 and 0.004 for nitrogen, phosphorous, potassium, calcium and magnesium, respectively. Predicting biochemicals on a test data set, using regression models developed from a training data set. resulted in R2 values ranging from 0. 15 to 0.70. The error of prediction (RSE) in the test data set was 0.08 (+ 10.25% of mean), 0.05 (+ 5.2% of mean), 0.02 (+ 11.11% of mean), 0.05 (+ 11.6% of mean) and 0.03 (+ 15% of mean) for nitrogen, potassium, phosphorous. calcium and magnesium, respectively, using CRDR. When data was partitioned into species groups, the R2 increased significantly to >0.80. With high-quality radiometric and geometric calibration of hyperspectral imagery, the techniques applied in this study (i.e. continuum removal on absorption features) may also be applied on data acquired by airborne and spacebome imaging spectrometers to predict and ultimateIy to map the concentration of macronutrients in tropical rangelands. Numéro de notice : A2004-020 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.11.001 En ligne : https://doi.org/10.1016/j.rse.2003.11.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26548
in Remote sensing of environment > vol 89 n° 3 (15/02/2004) . - pp 393 - 408[article]Mapping coal fires based on OMIS1 thermal infrared band image / Y. Wan in International Journal of Remote Sensing IJRS, vol 25 n° 3 (February 2004)
[article]
Titre : Mapping coal fires based on OMIS1 thermal infrared band image Type de document : Article/Communication Auteurs : Y. Wan, Auteur ; W. Deng, Auteur ; Y. Yan, Auteur Année de publication : 2004 Article en page(s) : pp 593 - 602 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Chine
[Termes IGN] image hyperspectrale
[Termes IGN] image thermique
[Termes IGN] incendie
[Termes IGN] réalité de terrain
[Termes IGN] température de surfaceRésumé : (Auteur) OMIS1 (Operative Modular Imaging Spectrometer) is a new imaging spectrometer designed by Shanghai Institute of Technical Physics, and has eight thermal infrared bands (nos. 105112). This paper presents the use of images obtained from these thermal infrared bands to detect and map coal fires in northwestern China. According to experiments in the Rujigou area of Ningxia municipality, each thermal infrared band has high correlation (>0.939). Regression analysis of land surface temperature (LST) with the pixel value in each thermal infrared band indicates: (1) for images acquired at daytime (from 11:00 to 13:00 local time), the first four thermal infrared bands have a linear relationship, and the final four bands have an exponential relationship; (2) for images acquired in the early morning (from 06:00 to 07:30), each band has a linear relationship. In conclusion, the ground temperature map is based on the 107th band of morning time. The mapping error is greatly determined by the quantity and precision of ground data measured synchronously. Numéro de notice : A2004-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000150112 En ligne : https://doi.org/10.1080/0143116031000150112 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26592
in International Journal of Remote Sensing IJRS > vol 25 n° 3 (February 2004) . - pp 593 - 602[article]Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 080-04031 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Unsupervised 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)
[article]
Titre : Unsupervised classification of hyperspectral data: an ICA mixture model based approach Type de document : Article/Communication Auteurs : Chintan A. Shah, Auteur ; M.K. Arora, Auteur ; P.K. Varshney, Auteur Année de publication : 2004 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] classification non dirigée
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classificationRésumé : (Auteur) Conventional unsupervised classification algorithms that model the data in each class with a multivariate Gaussian distribution are often inappropriate, as this assumption is frequently not satisfied by the remote sensing data. In this Letter, a new algorithm based on independent component analysis (ICA) is presented. The ICA mixture model (ICAMM) algorithm that models class distributions as non-Gaussian densities has been employed for unsupervised classification of a test image from the AVIRIS sensor. A number of feature-extraction techniques have also been examined that serve as a preprocessing step to reduce the dimensionality of the hyperspectral data. The proposed ICAMM algorithm results in significant increase in the classification accuracy over that obtained from the conventional K-means algorithm for land cover classification. Numéro de notice : A2004-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160310001618040 En ligne : https://doi.org/10.1080/01431160310001618040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26588
in International Journal of Remote Sensing IJRS > vol 25 n° 2 (January 2004)[article]Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 080-04021 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Hyperspectral 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)PermalinkUtilisation de simulations d'images hyperspectrales à partir de base de données 3D pour la spécification de futurs capteurs spatiaux / Audrey Malherbe (2004)PermalinkGeometric processing of hyperspectral image data acquired by VIFIS on board light aircraft / Y. Gu in International Journal of Remote Sensing IJRS, vol 24 n° 23 (December 2003)PermalinkSpectral reflectance characterization of shallow lakes from the Brazilian pantanal wetlands with field and airborne hyperspectral data / L.S. Galvao in International Journal of Remote Sensing IJRS, vol 24 n° 21 (November 2003)PermalinkA credit assignment approach to fusing classifiers of multiseason hyperspectral imagery / C. Bachmann in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)PermalinkImproving the performance of classifiers in high-dimensional remote sensing applications: an adaptive resampling strategy for error-prone exemplars / C. Bachmann in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkSpectral resolution requirements for mapping urban areas / Martin Herold in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)PermalinkAnalysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: Comparison between an Airborne (AVIRIS) and a spaceborne (Hyperion) sensor / M.L. Smith in IEEE Transactions on geoscience and remote sensing, vol 41 n° 6 (June 2003)PermalinkA CCD camera-based hyperspectral imaging system for stationary and airborne applications / C. Yang in Geocarto international, vol 18 n° 2 (June - August 2003)Permalink