关键词: 纹理, 相对全变差, 均值漂移, 遥感图像, 分割

Abstract: Abstract: Focused on the issue that the precise segmentation of remote sensing images which contain complex textures is always difficult, a novel algorithm which combined remote sensing image segmentation with texture removal was proposed. Firstly, the method of texture removal with relative total variation was improved in this paper. A new norm constraint was introduced to the relative total variation algorithm, which helped enhance the major structures in images while removing textures. Meanwhile, the improved texture removal method could assist the following image segmentation. Secondly, a mean shift algorithm was used to segment the remote sensing images after texture removal by unsupervised clustering. The proposed segmentation algorithm of remote sensing images was tested in different remote sensing images. The experimental results demonstrated that the proposed texture removal and image segmentation method could split the main objects in very high resolution remote sensing images. The proposed method obtained better results compared with other methods of remote sensing image segmentation which segmented images without texture removal or segmented remote sensing images combining with other texture removal methods. The proposed method in this paper could reduce the influences of textures on image segmentation and improve the accuracy of remote sensing image segmentation.

Key words: texture, relative total variation, mean shift, remote sensing image, segmentation