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Classification of urban tree species using hyperspectral imagery / R. Jensen in Geocarto international, vol 27 n° 5 (August 2012)  

Public [article]  inGeocarto international > vol 27 n° 5 (August 2012) . - pp 443 - 458 Titre : Classification of urban tree species using hyperspectral imagery Type de document : Article/Communication Auteurs : R. Jensen, Auteur ; P. Hardin, Auteur ; A. Hardin, Auteur Année de publication : 2012 Article en page(s) : pp 443 - 458 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] analyse en composantes principales [Termes IGN] arbre (flore) [Termes IGN] arbre urbain [Termes IGN] espèce végétale [Termes IGN] flore urbaine [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] indice de végétation [Termes IGN] Utah (Etas-Unis) Résumé : (Auteur) Urban areas serve as humanity's principal habitat. Because of this, it is important to understand the biophysical components of the urban environment – including the urban forest. The goal of this study was to determine the potential to classify individual urban trees as a function of spectral features derived from airborne hyperspectral data. To determine this, 500 urban trees were identified (through fieldwork) in the built-up zone of Provo-Orem, Utah, USA. Visible and near infrared airborne hyperspectral imagery was collected over the same area. The 500 trees were identified on the images, and spectral features of each tree were extracted. Principal components, vegetation indices, band means, and band ratios were all used as features to discriminate between different tree species. The tree classification was 82% accurate when just the six principal components were used. Classification accuracy increased to 91.4% after combining vegetation indices, band mean values and band ratios. Numéro de notice : A2012-373 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.687400 Date de publication en ligne : 24/05/2012 En ligne : https://doi.org/10.1080/10106049.2012.687400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31819 [article]

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Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes / J. Im in Geocarto international, vol 27 n° 5 (August 2012)  

Public [article]  inGeocarto international > vol 27 n° 5 (August 2012) . - pp 373 - 393 Titre : Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes Type de document : Article/Communication Auteurs : J. Im, Auteur ; Zhong Lu, Auteur ; J. Rhee, Auteur ; R. Jensen, Auteur Année de publication : 2012 Article en page(s) : pp 373 - 393 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme génétique [Termes IGN] classification par réseau neuronal [Termes IGN] données lidar [Termes IGN] entropie [Termes IGN] image AISA+ [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] milieu urbain [Termes IGN] New York (Etats-Unis ; état) Résumé : (Auteur) The urban landscape is dynamic and complex. As improved remote sensing data in terms of spatial and spectral characteristics became available, more sophisticated methods have been adopted for urban applications. This study proposed and evaluated a classification model incorporating feature selection, artificial immune networks and parameter optimization. Information gain, a broadly applied feature selection metric used in data mining techniques such as decision trees, was used for feature selection. Two types of information gain – binary-class entropy and multiple-class entropy – were investigated. Artificial immune networks have been recently applied to remote sensing classification and have been proven useful especially when multiple parameters of the networks are optimized through a genetic algorithm. The proposed model was tested for urban classification using hyperspectral (i.e. AISA and Hyperion) and LiDAR data over two urban study sites. Results show that the model considerably reduced processing time (70%) for classification without significant accuracy decrease. Numéro de notice : A2012-369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.642898 Date de publication en ligne : 06/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.642898 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31815 [article]

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Estimating urban leaf area index (LAI) of individual trees with hyperspectral data / R. Jensen in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 5 (May 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 5 (May 2012) . - pp 495 - 504 Titre : Estimating urban leaf area index (LAI) of individual trees with hyperspectral data Type de document : Article/Communication Auteurs : R. Jensen, Auteur ; P. Hardin, Auteur ; A. Hardin, Auteur Année de publication : 2012 Article en page(s) : pp 495 - 504 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse spectrale [Termes IGN] arbre (flore) [Termes IGN] feuillu [Termes IGN] image hyperspectrale [Termes IGN] Leaf Area Index [Termes IGN] réflectance végétale [Termes IGN] Utah (Etas-Unis) [Termes IGN] zone urbaine Résumé : (Auteur) This study estimated leaf area index (LAI) of individual urban trees as a function of spectral features derived from airborne hyperspectral data. Candidate features included spectral indexes, principal components, and calibrated reflectance values. Hyperspectral images were acquired over Provo, Utah area, and LAI of 204 deciduous trees was measured in the field. These tree canopies were identified on the images, and spectral features were extracted using both whole canopy and mean-lit spectra techniques. Multiple regression and artificial neural networks were used to model leaf area and determine which spectral features were most strongly related to it. Results established that simple hyperspectral vegetation indexes explained more variation in urban tree LAI than either principal component scores or simple band reflectance values. The neural network model trained with a subset of those indexes explained more variation in LAI (R2 = 64.8 percent) than any of the multiple regression models tested. Numéro de notice : A2012-234 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.78.5.495 En ligne : https://doi.org/10.14358/PERS.78.5.495 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31680 [article]

Modelling house unit density from land cover metrics: a Midwestern US example / P. Hardin in Geocarto international, vol 23 n° 5 (October - November 2008)  

Public [article]  inGeocarto international > vol 23 n° 5 (October - November 2008) . - pp 393 - 411 Titre : Modelling house unit density from land cover metrics: a Midwestern US example Type de document : Article/Communication Auteurs : P. Hardin, Auteur ; M. Jackson, Auteur ; R. Jensen, Auteur Année de publication : 2008 Article en page(s) : pp 393 - 411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse discriminante [Termes IGN] densité de population [Termes IGN] densité du bâti [Termes IGN] données socio-économiques [Termes IGN] estimation statistique [Termes IGN] image Landsat-ETM+ [Termes IGN] Indiana (Etats-Unis) [Termes IGN] régression multiple [Termes IGN] spatial metrics Résumé : (Auteur) Geographic applications frequently require the gathering and analysis of socioeconomic data. For many nations, these data are normally collected through a census. However, during the intercensal period (5-10 years), these data lose their currency and must be updated. The objective of this project was to estimate housing unit density from Landsat ETM+ imagery in the Terre Haute, IN, USA, region. Modelling was done for 1945 census blocks in the study area containing 30 972 housing units. Landtype, as represented by six cluster classes, was used as the primary surrogate for housing unit density. The percentage of each landtype within the census blocks was calculated. Other landscape metrics representing landtype patch dominance and diversity were also calculated on a per-block basis. Housing unit density within the census block was then modelled as a function of those percentages and metrics using discriminant analysis and multiple regression. The simple correlation between the observed and modelled housing unit density was 0.79. The mean residual error produced by the model was 0.37 housing units per hectare. Copyright Taylor & Francis Numéro de notice : A2008-465 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040801950344 Date de publication en ligne : 05/09/2008 En ligne : https://doi.org/10.1080/10106040801950344 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29534 [article]

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Spectral analysis of coastal vegetation and land cover using AISA+ hyperspectral data / R. Jensen in Geocarto international, vol 22 n° 1 (March - May 2007)  

Public [article]  inGeocarto international > vol 22 n° 1 (March - May 2007) . - pp 17 - 28 Titre : Spectral analysis of coastal vegetation and land cover using AISA+ hyperspectral data Type de document : Article/Communication Auteurs : R. Jensen, Auteur ; P. Mausel, Auteur ; N. Dias, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 17 - 28 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] étalonnage des données [Termes IGN] image AISA+ [Termes IGN] image hyperspectrale [Termes IGN] mangrove [Termes IGN] réflectance végétale [Termes IGN] Texas (Etats-Unis) Résumé : (Auteur) This paper describes a spectral analysis of several coastal land cover types in South Padre Island, Texas using AISA+ hyperspectral remote sensing data. AISA+ hyperspectral data (1.5 metre) were acquired throughout the area on 9 March 2005. Data over mangrove areas were converted to percent reflectance using four 88 metre reflectance tarps (4%, 16%, 32% and 48%) and empirical line calibration. These data were then compared to percent reflectance values of other terrestrial features to determine the ability of AISA+ data to distinguish features in coastal environments. Results suggest that these data may be appropriate to discriminate coastal mangrove vegetation and provide researchers with high resolution spatial and spectral information to more effectively manage coastal ecosystems. Copyright Taylor & Francis Numéro de notice : A2007-457 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040701204354 Date de publication en ligne : 24/05/2007 En ligne : https://doi.org/10.1080/10106040701204354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28820 [article]

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