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- W2548651658 abstract "The inventory of economic forest planting on mountainous area is of great interest for the shareholders, ecologists, and governors. This study presents a novel object-based remote sensing image texture extraction method to aid the classification of mountain economic forest. Whereas the texture pattern of man-planted forest on mountainous area are similar with human fingerprint on remote sensing images, then we integrated the fingerprint recognition method with the image object-based GLCM (gray-level co-occurrence matrix) texture extraction method to classify economic forest plantation. The given method firstly enhances the texture feature of segmented image objects using a 2D Gabor filter; and then carries out an image binaryzation process to the filtered image objects; it lastly uses GLCM to calculate the texture characteristics of each image object. The classification result of the presented method shows a considerable classification accuracy increment comparing to the image object-based GLCM texture extraction method." @default.
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- W2548651658 date "2016-07-01" @default.
- W2548651658 modified "2023-09-24" @default.
- W2548651658 title "Texture feature extraction of mountain economic forest using high spatial resolution remote sensing images" @default.
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- W2548651658 doi "https://doi.org/10.1109/igarss.2016.7729816" @default.
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