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High-resolution Remote Sensing Image Segmentation Method with A Combination of spectrum, texture and shape Features
Abstract
Because of high-resolution remote sensing images have rich texture information, feature geometry and spatial information, this paper proposes high-resolution remote sensing image segmentation method with a combination of spectrum, texture and shape features.First, use contrast ehancement to enhance the contrast between various parts of the original image, and then use supervision Markov random field model to carry out texture segmentation of remote sensing images, at last, use Fractal net evolution approach to carry out segmentation of split of the original images(wherein, the obtained image texture segmentation result is considered as a priori knowledge on the process, and its role in the segmentation process is determined by set weights) and get the image segmentation results. Carry out experiment by using of QuickBird data of two regions of Yunnan, and implement comparison and quantitative evaluation of segmentation results under different experimental conditions, then the results show that the method is feasible and effective.
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