Centre de documentation
scientifique

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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2012051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A volumetric approach to population estimation using lidar remote sensing / Zhong Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 11 (November 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 11 (November 2011) . - pp 1145 - 1156 Titre : A volumetric approach to population estimation using lidar remote sensing Type de document : Article/Communication Auteurs : Zhong Lu, Auteur ; J. Im, Auteur ; L. Quackenbush, Auteur Année de publication : 2011 Article en page(s) : pp 1145 - 1156 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Denver [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] estimation statistique [Termes IGN] habitat urbain [Termes IGN] ilot [Termes IGN] lasergrammétrie [Termes IGN] population urbaine [Termes IGN] recensement démographique [Termes IGN] régression [Termes IGN] volume (grandeur) [Termes IGN] zone urbaine Résumé : (Auteur) This research investigated the applicability of lidar data for estimating population at the census block level using a volumetric approach. The study area, near the urban downtown area of Denver, Colorado, was selected since it includes dense distribution of different types of residential buildings. A modified morphological building detection algorithm was proposed to extract buildings from the lidar-derived surfaces. The extraction results showed that the modified morphological building detection algorithm can effectively recover building pixels occluded by nearby trees. The extracted buildings were further refined to residential buildings using parcel data. Two approaches (i.e., area- and volume-based) to population estimation were investigated at the census block level. Four regression models (i.e., simple linear regression, multiple linear regression, regression tree using one variable, and regression tree using multiple variables) were used to identify the relationship between census population and the area or volume information of the residential buildings. The volume-based models over-whelmingly outperformed the area-based models in the study area, and the models using multiple variables yielded more accurate estimation than the single variable models. The volume-based regression tree model using multiple variables yielded the most accurate estimations: R2 = 0.89, RMSE = 21 people, and RRMSE = 26.8 percent in the calibration site; and R2 = 0.80, RMSE = 27 people, and RRMSE = 30.1 percent in the validation site. As the results show, the volumetric approach using lidar remote sensing is effective for population estimation in regions with heterogeneous housing characteristics. Numéro de notice : A2011-448 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.11.1145 En ligne : https://doi.org/10.14358/PERS.77.11.1145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31226 [article]

A genetic algorithm approach to moving threshold optimization for binary change detection / J. Im in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 2 (February 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 2 (February 2011) . - pp 167 - 180 Titre : A genetic algorithm approach to moving threshold optimization for binary change detection Type de document : Article/Communication Auteurs : J. Im, Auteur ; Zhong Lu, Auteur ; J. Jensen, Auteur Année de publication : 2011 Article en page(s) : pp 167 - 180 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme génétique [Termes IGN] détection de changement [Termes IGN] image Quickbird [Termes IGN] seuillage d'image Résumé : (Auteur) This study investigated the use of a genetic algorithm (GA) approach, a widely used optimization method, to identify optimum thresholds for remote sensing-based binary change detection. Automated GA-based calibration models using a moving threshold window (MTW) were developed and tested using a case study. Two sets of the bi-temporal QuickBird imagery were used to evaluate the new optimization models. The GA-based models using MTW were free from the assumption of symmetry of thresholds for difference- or ratio-type of change-enhanced images, unlike traditional binary change detection methods, allowing more flexibility and efficiency in selecting optimum thresholds. Exhaustive search techniques using symmetric threshold window (STW) and MTW were evaluated for comparison. The stability of the GA-based models in terms of accuracy variation was also examined. The GA-based calibration models successfully identified optimum thresholds without a significant decrease in accuracy. The GA-based models using MTW outperformed the GA-based model using STW in both calibration and validation, revealing that optimum thresholds tended to be asymmetric. Multiple change-enhanced images generally resulted in better performance than single change-enhanced images based on the GA-based models. Numéro de notice : A2011-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.2.167 En ligne : https://doi.org/10.14358/PERS.77.2.167 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30828 [article]

A change detection model based on neighborhood correlation image analysis and decision tree classification / J. Im in Remote sensing of environment, vol 99 n° 3 (30/11/2005)  

Public [article]  inRemote sensing of environment > vol 99 n° 3 (30/11/2005) . - pp 326 - 340 Titre : A change detection model based on neighborhood correlation image analysis and decision tree classification Type de document : Article/Communication Auteurs : J. Im, Auteur ; J.R. Jensen, Auteur Année de publication : 2005 Article en page(s) : pp 326 - 340 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] classification par arbre de décision [Termes IGN] détection de changement [Termes IGN] données multitemporelles [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] Kappa de Cohen [Termes IGN] prise en compte du contexte [Termes IGN] voisinage (relation topologique) Résumé : (Auteur) This study introduces a change detection model based on Neighborhood Correlation Image (NCI) logic. It is based on the fact that the sail-, geographic area (e.g., a 3 x 3 pixel window) on two dates of imagery will tend to be highly correlated if little change has occurred, an uncorrelated when change occurs. Computing the piecewise correlation between two data sets provides valuable information regarding the location and numeric change value derived using contextual information within the specified neighborhood. Various neighborhood configuration (i.e., multi-level NCls) were explored in the study using high spatial resolution multispectral imagery: smaller neighborhood sizes provided some detailed change information (such as a new patios added to an existing building) at the cost of introducing some noise (such as change, shadows). Larger neighborhood sizes were useful for removing this noise but introduced some inaccurate change information (such as removing some linear feature changes). When combined with image classification using a machine learning decision tree (C5.0), classifications based on multi-level NCIs yielded superior results (e.g., using a 3-pixel circular radius neighborhood had a Kappa of 0.94), compared to the classification that did not incorporate NCIs (Kappa=0.86). Numéro de notice : A2005-460 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2005.09.008 En ligne : https://doi.org/10.1016/j.rse.2005.09.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27596 [article]