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Fusing Ikonos images by a four-band wavelet transformation method / Wei Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 11 (November 2007)
[article]
Titre : Fusing Ikonos images by a four-band wavelet transformation method Type de document : Article/Communication Auteurs : Wei Shi, Auteur ; C. Zhu, Auteur ; S. Zhu, Auteur Année de publication : 2007 Article en page(s) : pp 1285 - 1292 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] fusion d'images
[Termes IGN] image à résolution métrique
[Termes IGN] image Ikonos
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] transformation en ondelettesRésumé : (Auteur) This paper presents a wavelet transformation-based method for fusing high-resolution satellite images. First, a multi-band wavelet fusion method, specifically the four-band wavelet method, is proposed for fusing one-meter panchromatic and four-meter multi-spectral Ikonos images. Second, the fusion experiments are undertaken by using the proposed four-band wavelet method. The results are compared with fused images from other methods, such as two-band wavelet and IHS methods. The results are evaluated based on both visual evaluation and a quantitative analysis, by using several assessing parameters and a new evaluation method: the profile intensity curve. Third, as an application of the fused images, they are applied for recognition and identification of features in urban area. The quantitative analysis demonstrates that four-band wavelet transformation method provides improved fused images. Copyright ASPRS Numéro de notice : A2007-519 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.11.1285 En ligne : https://doi.org/10.14358/PERS.73.11.1285 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28882
in Photogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 11 (November 2007) . - pp 1285 - 1292[article]Weighting function alternatives for a subpixel allocation model / Y. Makido in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 11 (November 2007)
[article]
Titre : Weighting function alternatives for a subpixel allocation model Type de document : Article/Communication Auteurs : Y. Makido, Auteur ; A. Shortridge, Auteur Année de publication : 2007 Article en page(s) : pp 1233 - 1240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] allocation
[Termes IGN] analyse infrapixellaire
[Termes IGN] autocorrélation spatiale
[Termes IGN] classification barycentrique
[Termes IGN] image Ikonos
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] optimisation (mathématiques)
[Termes IGN] précision de la classificationRésumé : (Auteur) This study investigates the “pixel-swapping” optimization algorithm proposed by Atkinson for predicting subpixel land- cover distribution. Two limitations of this method are assessed: the arbitrary spatial range value and the arbitrary exponential model for characterizing spatial autocorrelation. Various alternative weighting functions are evaluated. For this assessment, two different simulation models are employed to develop spatially autocorrelated binary class raster maps. These rasters are then resampled to generate sets of representative medium-resolution class maps. Prior to conducting the subpixel allocation, the relationship between cell resolution and spatial autocorrelation, as measured by Moran’s I, is evaluated. It is discovered that the form of this relationship depends upon the simulation model. For all tested weighting functions (Nearest Neighbor, Gaussian, Exponential, and IDW), the pixel swapping method increased classification accuracy compared with the initial random allocation of subpixels. Nearest Neighbor allocation performs as well as the more complex models of spatial structure. Copyright ASPRS Numéro de notice : A2007-514 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.11.1233 En ligne : http://dx.doi.org/10.14358/PERS.73.11.1233 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28877
in Photogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 11 (November 2007) . - pp 1233 - 1240[article]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]The impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes / T. Koukal in Remote sensing of environment, vol 110 n° 4 (30/10/2007)
[article]
Titre : The impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes Type de document : Article/Communication Auteurs : T. Koukal, Auteur ; F. Suppan, Auteur ; W. Schneider, 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 431 - 437 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Autriche
[Termes IGN] classification barycentrique
[Termes IGN] étalonnage radiométrique
[Termes IGN] forêt
[Termes IGN] image Landsat-TM
[Termes IGN] point d'appui
[Termes IGN] régression linéaireRésumé : (Auteur) The k-nearest-neighbour (kNN) algorithm is widely applied for the estimation of forest attributes using remote sensing data. It requires a large amount of reference data to achieve satisfactory results. Usually, the number of available reference plots for the kNN-prediction is limited by the size of the area covered by a terrestrial reference inventory and remotely sensed imagery collected from one overflight. The applicability of kNN could be enhanced if adjacent images of different acquisition dates could be used in the same estimation procedure. Relative radiometric calibration is a prerequisite for this. This study focuses on two empirical calibration methods. They are tested on adjacent LANDSAT TM scenes in Austria. The first, quite conventional one is based on radiometric control points in the overlap area of two images and on the determination of transformation parameters by linear regression. The other, recently developed method exploits the kNN-cross-validation procedure. Performance and applicability of both methods as well as the impact of phenology are discussed. Copyright Elsevier Numéro de notice : A2007-411 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.08.016 En ligne : https://doi.org/10.1016/j.rse.2006.08.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28774
in Remote sensing of environment > vol 110 n° 4 (30/10/2007) . - pp 431 - 437[article]Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)
[article]
Titre : Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation Type de document : Article/Communication Auteurs : A. Agrawal, Auteur ; N. Kumar, Auteur ; M. Radhakrishna, Auteur Année de publication : 2007 Article en page(s) : pp 4597 - 4608 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] ERDAS Imagine
[Termes IGN] image IRS-LISS
[Termes IGN] image multibande
[Termes IGN] incertitude des données
[Termes IGN] Inde
[Termes IGN] Kappa de Cohen
[Termes IGN] Perceptron multicouche
[Termes IGN] sous ensemble flouRésumé : (Auteur) A supervised neural network classification model based on rough-fuzzy membership function, weak fuzzy similarity relation, multilayer perceptron, and back-propagation algorithm is proposed. The described model is capable of dealing with rough uncertainty as well as fuzzy uncertainty associated with the classification of multispectral images. The concept of weak fuzzy similarity relation is used for generation of fuzzy equivalence classes during the calculation of rough-fuzzy membership function. The model allows efficient modelling of indiscernibility and fuzziness between patterns by appropriate weights being assigned using the back-propagated errors depending upon the rough-fuzzy membership values at the corresponding outputs. The effectiveness of the proposed model is demonstrated on classification problem of IRS-P6 LISS IV image of Allahabad area. The results are compared with statistical (minimum distance to means), conventional Multi-Layer Perceptron (MLP) and Fuzzy Multi-Layer Perceptron (FMLP) models. The better overall accuracy, user's and producer's accuracies and kappa coefficient of the proposed classifier in comparison to other considered models demonstrate the effectiveness of this model in multispectral image classification. Copyright Taylor & Francis Numéro de notice : A2007-449 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701244898 En ligne : https://doi.org/10.1080/01431160701244898 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28812
in International Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4597 - 4608[article]Réservation
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