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• W2049987478 abstract "A Semivariogram, as defined in geostatistics, is a powerful tool for texture extraction of remotely sensed images. However, the traditional texture features extracted by a semivariogram are generally for pixel-based classification. Moreover, most studies have been based on the original computation mode of semivariogram and discrete semivariance values. This article describes a set of semivariogram texture features STFs based on the mean square root pair difference SRPD to improve the accuracy of object-oriented classification OOC in QuickBird images. The adaptive parameters for the calculation of a semivariogram were first derived from semivariance analysis, including directions, moving window size, and lag distance. Then, 22 STFs were extracted from the discrete and mean/standard deviation semivariance, and 15 features were selected from the extracted STFs based on feature optimization. Then five grey-level co-occurrence matrix GLCM texture features mean, homogeneity, contrast, angular second moment, and entropy were calculated based on segmented image objects using the panchromatic band. A comparison of classification results demonstrates that the STFs described in this article are useful supplement information for the spectral OOC, and the spectral + STFs classification method can be used to obtain a higher classification accuracy than can the combination of spectral and GLCM features." @default.
• W2049987478 created "2016-06-24" @default.
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• W2049987478 date "2013-02-04" @default.
• W2049987478 modified "2023-10-16" @default.
• W2049987478 title "Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram" @default.
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• W2049987478 doi "https://doi.org/10.1080/01431161.2012.759298" @default.
• W2049987478 hasPublicationYear "2013" @default.
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