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Abstract
The traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. This method firstly uses middle semantic which can reflect an image’s visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application.
Highlights
GEOBIA has been the mainstream method for processing high spatial resolution remote sensing images [1, 2]
Discussion and Conclusion e proposed stratified segmentation method combines hue and hue layer textures to divide scenes, which is theoretically more similar to the human visual mechanism. rough scene division, the complex of entire image was effectively reduced. This method is strongly universal, so can be used, the divisions are of accuracy to some extent. e result shows that this method can improve the final classification accuracy effectively, especially for largesized images wherein the aggregation phenomenon is clear
E method can significantly aid in remote sensing image classification and feature extraction
Summary
GEOBIA has been the mainstream method for processing high spatial resolution remote sensing images [1, 2]. Many methods have been used to select optimal parameters for multiscale segmentation [16,17,18,19,20,21,22,23,24,25]; optimal segmentation parameters for an overall image may not suitable for different objects when processing large heterogeneous images [26, 27]. A key issue that remains to be resolved is to determine a suitable segmentation scale that allows different objects and phenomena to be characterized in a single image [28, 29]. Observations indicate that there is a tendency: the same types of objects often have similar spatial scale and often aggregate in the same area
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