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.
['Panpan Yang', 'Zhiting Hou', 'Xiaolong Liu', 'Zhengtao Shi']
Texture feature extraction of mountain economic forest using high spatial resolution remote sensing images