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Optimizing image resolution to maximize the accuracy of hard classification / K.R. Mccloy in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 8 (August 2007)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 8 (August 2007) . - pp 893 - 903 Titre : Optimizing image resolution to maximize the accuracy of hard classification Type de document : Article/Communication Auteurs : K.R. Mccloy, Auteur ; P.K. Bocher, Auteur Année de publication : 2007 Article en page(s) : pp 893 - 903 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] limite de résolution géométrique [Termes IGN] matrice de confusion [Termes IGN] précision de la classification [Termes IGN] théorie des erreurs [Termes IGN] variance Résumé : (Auteur) There are three strategies by which the accuracy of classification can be improved after the imagery that will be used for the classification has been chosen. These are to improve the definition of the class decision surfaces, to maximize the between class distances, and to reduce the within class variances. This paper reports on work done to investigate the relationship between classification accuracy and within class variances, where generally accepted measures of accuracy derived from the Confusion Matrix are used as the indicators of classification accuracy. This paper shows that the within class variances are a function of image resolution, and it provides a mechanism based on the Average Local Variance (ALV) function to find the resolution that will yield the highest relative within field classification accuracy by minimizing the within class variances. Copyright ASPRS Numéro de notice : A2007-369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.8.893 En ligne : http://dx.doi.org/10.14358/PERS.73.8.893 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28732 [article]

Integrating fine scale information in super-resolution land-cover mapping / A. Boucher in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 8 (August 2007)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 8 (August 2007) . - pp 913 - 921 Titre : Integrating fine scale information in super-resolution land-cover mapping Type de document : Article/Communication Auteurs : A. Boucher, Auteur ; P.C. Kyriakidis, Auteur Année de publication : 2007 Article en page(s) : pp 913 - 921 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse infrapixellaire [Termes IGN] carte d'occupation du sol [Termes IGN] krigeage [Termes IGN] limite de résolution géométrique [Termes IGN] variogramme Résumé : (Auteur) Super-resolution or sub-pixel class mapping is the task of providing fine spatial resolution maps of, for example, landcover classes, from satellite sensor measurements obtained at a coarser spatial resolution. Often, the only information available consists of coarse class fraction data, typically obtained through spectral unmixing. This paper shows how to integrate, in addition to such coarse fractions, class labels at a set of fine pixels obtained independent of the satellite sensor measurements. The integration of such fine spatial resolution information is achieved within the Indicator Kriging formalism in either a prediction or simulation mode. The spatial dissimilarity or texture of class labels at the fine (target) resolution is quantified in a non-parametric way from an analog scene using a set of experimental indicator semivariogram maps. The output of the proposed procedure consists of maps of probabilities of class occurrence, or of a series of simulated class maps characterizing the inherent spatial uncertainty in the super-resolution mapping process. Numéro de notice : A2007-370 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.8.913 En ligne : http://dx.doi.org/10.14358/PERS.73.8.913 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28733 [article]

Assessing alternatives for modelling the spatial distribution of multiple land-cover classes at sub-pixel scales / Y. Makido in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 8 (August 2007)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 8 (August 2007) . - pp 935 - 943 Titre : Assessing alternatives for modelling the spatial distribution of multiple land-cover classes at sub-pixel scales Type de document : Article/Communication Auteurs : Y. Makido, Auteur ; A. Shortridge, Auteur ; P. Messina, Auteur Année de publication : 2007 Article en page(s) : pp 935 - 943 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse infrapixellaire [Termes IGN] autocorrélation spatiale [Termes IGN] distribution spatiale [Termes IGN] image Landsat-ETM+ Résumé : (Auteur) We introduce and evaluate three methods for modeling the spatial distribution of multiple land-cover classes at subpixel scales: (a) sequential categorical swapping, (b) simultaneous categorical swapping, and (c) simulated annealing. Method 1, a modification of a binary pixel-swapping algorithm, allocates each class in turn to maximize internal spatial autocorrelation. Method 2 simultaneously examines all pairs of cell-class combinations within a pixel to determine the most appropriate pairs of sub-pixels to swap. Method 3 employs simulated annealing to swap cells. While convergence is relatively slow, Method 3 offers increased flexibility. Each method is applied to a classified Landsat-7 ETM dataset that has been resampled to a spatial resolution of 210 m, and evaluated for accuracy performance and computational efficiency. Numéro de notice : A2007-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.8.935 En ligne : http://dx.doi.org/10.14358/PERS.73.8.935 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28734 [article]