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Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression / J. Walton in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 10 (October 2008) . - pp 1213 - 1222 Titre : Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression Type de document : Article/Communication Auteurs : J. Walton, Auteur Année de publication : 2008 Article en page(s) : pp 1213 - 1222 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] image Landsat-ETM+ [Termes IGN] milieu urbain [Termes IGN] occupation du sol [Termes IGN] régression [Termes IGN] séparateur à vaste marge [Termes IGN] surface imperméable Résumé : (Auteur) Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (reflectance, tasseled cap, and both reflectance and tasseled cap plus thermal) were compared for their effectiveness with each of the methods. Thirty different training site number and size combinations were also tested. Support vector regression on the tasseled cap bands was found to be the best estimator for urban forest canopy cover, while Cubist performed best using the reflectance plus tasseled cap band combination when predicting impervious surface cover. More training data partitioned in many small training sites generally produces better estimation results. Copyright ASPRS Numéro de notice : A2008-374 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.74.10.1213 En ligne : https://doi.org/10.14358/PERS.74.10.1213 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29367 [article]

Neuro-fuzzy based analysis of hyperspectral imagery / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 10 (October 2008) . - pp 1235 - 1247 Titre : Neuro-fuzzy based analysis of hyperspectral imagery Type de document : Article/Communication Auteurs : F. Qiu, Auteur Année de publication : 2008 Article en page(s) : pp 1235 - 1247 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Chine [Termes IGN] classification floue [Termes IGN] classification hybride [Termes IGN] classification par réseau neuronal [Termes IGN] découverte de connaissances [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale Résumé : (Auteur) A neuro-fuzzy system, namely Gaussian Fuzzy Learning Vector Quantization (GFLVQ), was developed based on the synergy of a neural network and a fuzzy system. GFLVQ is both a fuzzy neural network and a neural fuzzy system with supervised learning and unsupervised self-organizing capabilities. In this paper, GFLVQ was further improved to efficiently and effectively process hyperspectral data through training data informed initialization and a simplified fuzzy learning algorithm. A geovisualization tool was developed to facilitate knowledge discovery and understanding of the hyperspectral image. A case study was conducted using a Hyperion image. The results obtained from the improved neuro-fuzzy system were found to be significantly better than those from conventional statistics-based and endmember-based classifiers. The fuzzy spectral profiles produced from the geovisualization tool provided an extra insight into the neuro-fuzzy learning process, further opening up the black box of the neural network. Copyright ASPRS Numéro de notice : A2008-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.10.1235 En ligne : https://doi.org/10.14358/PERS.74.10.1235 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29368 [article]

Genetic algorithms for the calibration of cellular automata urban growth modeling / J. Shan in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 10 (October 2008) . - pp 1267 - 1277 Titre : Genetic algorithms for the calibration of cellular automata urban growth modeling Type de document : Article/Communication Auteurs : J. Shan, Auteur ; S. Alkheder, Auteur ; Jing Wang, Auteur Année de publication : 2008 Article en page(s) : pp 1267 - 1277 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] automate cellulaire [Termes IGN] classification par algorithme génétique [Termes IGN] croissance urbaine Résumé : (Auteur) This paper discusses the use of genetic algorithms to enhance the efficiency of transition rule calibration in cellular automata urban growth modeling. The cellular automata model is designed as a function of multitemporal satellite imagery and population density. Transition rules in the model identify the required neighborhood urbanization level for a test pixel to develop to urban. Calibration of the model is initially performed by exhaustive search, where the entire solution space is examined to find the best set of rule values. This method is computationally extensive and needs to consider all possible combinations for the transition rules. The rise in the number of variables will exponentially increase the time required for running and calibrating the model. This study introduces genetic algorithms as an effective solution to the calibration problem. It is shown that the genetic algorithms are able to produce modeling results close to the ones obtained from the exhaustive search in a time effective manner. Optimal rule values can be reached within the early generations of genetic algorithms. It is expected that genetic algorithms will significantly benefit urban modeling problems with larger set of input data and bigger solution spaces. Copyright ASPRS Numéro de notice : A2008-376 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.10.1267 En ligne : https://doi.org/10.14358/PERS.74.10.1267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29369 [article]