FEATURE MODELLING OF HIGH RESOLUTION REMOTE SENSING IMAGES CONSIDERING SPATIAL AUTOCORRELATION

To deal with the problem of spectral variability in high resolution satellite images, this paper focuses on the analysis and modelling of spatial autocorrelation feature. The semivariograms are used to model spatial variability of typical object classes while Getis statistic is used for the analysis...

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Bibliographic Details
Published in:International archives of the photogrammetry, remote sensing and spatial information sciences. remote sensing and spatial information sciences., 2012-08, Vol.XXXIX-B3, p.467-472
Main Authors: Chen, Y. X., Qin, K., Liu, Y., Gan, S. Z., Zhan, Y.
Format: Article
Language:English
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Summary:To deal with the problem of spectral variability in high resolution satellite images, this paper focuses on the analysis and modelling of spatial autocorrelation feature. The semivariograms are used to model spatial variability of typical object classes while Getis statistic is used for the analysis of local spatial autocorrelation within the neighbourhood window determined by the range information of the semivariograms. Two segmentation experiments are conducted via the Fuzzy C-Means (FCM) algorithm which incorporates both spatial autocorrelation features and spectral features, and the experimental results show that spatial autocorrelation features can effectively improve the segmentation quality of high resolution satellite images.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprsarchives-XXXIX-B3-467-2012