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Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium / F.M.B. Van Coillie in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
[article]
Titre : Feature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium Type de document : Article/Communication Auteurs : F.M.B. Van Coillie, Auteur ; L.P.C. Verbeke, Auteur ; R.R. DE Wulf, Auteur Année de publication : 2007 Conférence : ForestSat 2007, forests and remote sensing : methods and operational tools 05/11/2007 07/11/2007 Montpellier France Article en page(s) : pp 476 - 487 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme génétique
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'objet
[Termes IGN] Flandre (Belgique)
[Termes IGN] forêt tempérée
[Termes IGN] image Ikonos
[Termes IGN] segmentation d'imageRésumé : (Auteur) Obtaining detailed information about the amount of forest cover is an important issue for governmental policy and forest management. This paper presents a new approach to update the Flemish Forest Map using IKONOS imagery. The proposed method is a three-step object-oriented classification routine that involves the integration of 1) image segmentation, 2) feature selection by Genetic Algorithms (GAs) and 3) joint Neural Network (NN) based object-classification. The added value of feature selection and neural network combination is investigated. Results show that, with GA-feature selection, the mean classification accuracy (in terms of Kappa Index of Agreement) is significantly higher (p Numéro de notice : A2007-412 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2007.03.020 En ligne : https://doi.org/10.1016/j.rse.2007.03.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28775
in Remote sensing of environment > vol 110 n° 4 (30/10/2007) . - pp 476 - 487[article]Classified road detection from satellite images based on perceptual organization / J. Yang in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)
[article]
Titre : Classified road detection from satellite images based on perceptual organization Type de document : Article/Communication Auteurs : J. Yang, Auteur ; R.S. Wang, Auteur Année de publication : 2007 Article en page(s) : pp 4653 - 4669 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme génétique
[Termes IGN] axe médian
[Termes IGN] classification automatique
[Termes IGN] extraction du réseau routier
[Termes IGN] image satellite
[Termes IGN] lissage de courbe
[Termes IGN] méthode heuristique
[Termes IGN] objet géographique
[Termes IGN] primitive géométriqueRésumé : (Auteur) Extracting roads from satellite images is an important task in both research and practice. This work presents an improved model for road detection based on the principles of perceptual organization and classification fusion in human vision system (HVS). The model consists of four levels: pixels, primitives, structures and objects, and two additional sub-processes: automatic classification of road scenes and global integration of multiform roads. Based on the model, a novel algorithm for detecting roads from satellite images is also proposed, in which two types of road primitives, namely blob-like primitive and line-like primitive are defined, measured, extracted and linked using different methods for dissimilar road scenes. A hierarchical search strategy driven by saliency measurement is adopted in both linking processes. The blob primitives are linked using heuristic grouping and the line primitives are connected through genetic algorithm (GA) evolution. Finally, all of the linked road segments are normalized with centre-main lines and integrated into global smooth road curves through tensor voting. Experimental results show that the algorithm is capable of detecting multiform roads from real satellite images with high adaptability and reliability. Copyright Taylor & Francis Numéro de notice : A2007-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701250382 En ligne : https://doi.org/10.1080/01431160701250382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28819
in International Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4653 - 4669[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-07111 RAB Revue Centre de documentation En réserve L003 Disponible Multitemporel fuzzy classification model based on class transition possibilities / G.L.A. Mota in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 3 (August 2007)
[article]
Titre : Multitemporel fuzzy classification model based on class transition possibilities Type de document : Article/Communication Auteurs : G.L.A. Mota, Auteur ; R. Feitosa, Auteur ; H.L.C. Coutinho, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 186 - 200 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme génétique
[Termes IGN] Brésil
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification floue
[Termes IGN] image Landsat
[Termes IGN] image multitemporelle
[Termes IGN] modélisation spatialeRésumé : (Auteur) This paper proposes a new method to model temporal knowledge and to combine it with spectral and spatial knowledge within an integrated fuzzy automatic image classification framework for land-use land-cover map update applications. The classification model explores not only the object features, but also information about its class at a previous date. The method expresses temporal class dependencies by means of a transition diagram, assigning a possibility value to each class transition. A Genetic Algorithm (GA) carries out the class transition possibilities estimation. Temporal and spectral/spatial classification results are combined by means of fuzzy aggregation. The improvement achieved by the use of multitemporal knowledge rather than a pure monotemporal approach was assessed in a real application using LANDSAT images from Midwest Brazil. The experiments showed that the use of temporal knowledge markedly improved the classification performance, in comparison to a conventional single-time classification. A further observation was that multitemporal knowledge may subsume the knowledge related to steady spatial attributes whose values do not significantly change over time. Copyright ISPRS Numéro de notice : A2007-368 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.04.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28731
in ISPRS Journal of photogrammetry and remote sensing > vol 62 n° 3 (August 2007) . - pp 186 - 200[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-07051 SL Revue Centre de documentation Revues en salle Disponible Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm / J. Tang in International Journal of Remote Sensing IJRS, vol 28 n°15-16 (August 2007)
[article]
Titre : Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm Type de document : Article/Communication Auteurs : J. Tang, Auteur ; L. Wang, Auteur ; Z. Yao, Auteur Année de publication : 2007 Article en page(s) : pp 3255 - 3271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme génétique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] chaîne de Markov
[Termes IGN] Chine
[Termes IGN] image Landsat
[Termes IGN] modèle de Markov
[Termes IGN] prévision
[Termes IGN] urbanisationRésumé : (Auteur) The landscape pattern of Daqing City, China, has undergone a significant change over the past 20 years, as a result of the rapid urbanization process. To understand how urbanization has influenced the landscape in Daqing City, the largest base of the petrochemical industry in China, we conducted a series of spatial analyses with landscape pattern maps obtained from Landsat images in 1979, 1990 and 2000. Results indicate that a substantial urban area has been extended during the past two decades, along with the shrinking of wetland and woodland. Spatio-temporal optimization is not a trivial task in developing landscape models. In previous studies, the optimization of spatial and temporal factors was achieved separately, because of the difficulty in formulating them together in a single model. In this study, we adapted the traditional Markov model by obtaining model parameters and neighbourhood rules from a modified genetic algorithm (GA). Model performance was evaluated between the empirical landscape map from the Landsat image and the simulated landscape map from the models. Over three simulation runs, the global deviation (GD) for the three models was 1.37, 1.10 and 1.15, respectively. This result shows that the Markov model and the GA together are able to effectively capture the spatio-temporal trend in the landscape pattern associated with urbanization for this region. The future landscape distribution in 2010, 2030 and 2050 was derived using a spatial Markov model (SMM) for further urban change and planning research. Copyright Taylor & Francis Numéro de notice : A2007-357 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600962749 En ligne : https://doi.org/10.1080/01431160600962749 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28720
in International Journal of Remote Sensing IJRS > vol 28 n°15-16 (August 2007) . - pp 3255 - 3271[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-07091 RAB Revue Centre de documentation En réserve L003 Disponible Feature extractions for small sample size classification problem / B.C. Kuo in IEEE Transactions on geoscience and remote sensing, vol 45 n° 3 (March 2007)
[article]
Titre : Feature extractions for small sample size classification problem Type de document : Article/Communication Auteurs : B.C. Kuo, Auteur ; K.Y. Chang, Auteur Année de publication : 2007 Article en page(s) : pp 756 - 764 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme génétique
[Termes IGN] classification dirigée
[Termes IGN] décomposition du pixel
[Termes IGN] détection de contours
[Termes IGN] reconnaissance de formes
[Termes IGN] valeur propreRésumé : (Auteur) Much research has shown that the definitions of within-class and between-class scatter matrices and regularization technique are the key components to design a feature extraction for small sample size problems. In this paper, we illustrate the importance of another key component, eigenvalue decomposition method, and a new regularization technique was proposed. In the hyperspectral image experiment, the effects of these three components of feature extraction are explored on ill-posed and poorly posed conditions. The experimental results show that different regularization methods need to cooperate with different eigenvalue decomposition methods to reach the best performance, the proposed regularization method, regularized feature extraction (RFE) outperform others, and the best feature extraction for a small sample size classification problem is RFE with nonparametric weighted scatter matrices. Numéro de notice : A2007-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.885074 En ligne : https://doi.org/10.1109/TGRS.2006.885074 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28453
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 3 (March 2007) . - pp 756 - 764[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-07031 RAB Revue Centre de documentation En réserve L003 Disponible Optimisation en traitement du signal et de l'image / Patrick Siarry (2007)PermalinkA spatial approach to forest-management optimization: linking GIS and multiple objective genetic algorithms / E.I. Ducheyne in International journal of geographical information science IJGIS, vol 20 n° 8 (september 2006)PermalinkA new method to determine near surface air temperature from satellite observations / Ranjit Singh in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)PermalinkCumul de mesures de télémétrie laser sur satellites / Arnaud Pollet (2006)PermalinkPermalinkIntegration of genetic algorithms and GIS for optimal location search / X. Li in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)PermalinkAutomatic determination of the optimum generic sensor model based on genetic algorithm concepts / F. Samadzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 3 (March 2005)PermalinkModeling reality: how computers mirror life / Iwo Bialynicki-Birula (2004)PermalinkAutomated photogrammetric network design using genetic algorithms / G. Olague in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 5 (Mai 2002)PermalinkECAI 2002, 15th European Conference on Artificial Intelligence, July 21-26, Lyon, France / Frank Van Harmelen (2002)Permalink