基于改进的均值漂移算法的高分辨率遥感影像分割方法研究

Research on High-resolution Remote Sensing Image Segmentation Based on Improved Mean Shift

影像分割是面向对象信息提取的关键,其分割结果的精度高低将对信息提取结果精度产生重要影响。本文在研究均值漂移(Mean shift)算法的基础上,对图像的多维特征向量加以改进。该改进算法先通过改进颜色特征距离公式来对高分辨率遥感影像进行预处理,然后加入纹理特征向量,并设置纹理特征的参数值和迭代次数,进而对高分辨率遥感影像进行分割。文章采用该改进的均值漂移算法对遥感影像进行分割实验,并与业界公认的ecognition软件的分割结果进行对比,实验结果表明:该改进算法得到的分割精度更高,能得到更好的分割结果。

Image segmentation is the key of object-oriented information extraction, and the accuracy of the segmentation results will have an important influence on the accuracy of information extraction. Based on the research of mean shift algorithm, the multidimensional feature vector of image was improved. Firstly, the improved algorithm adopted the improving color feature distance formula to the pretreatment of high-resolution remote sensing image, then it adopted adding the texture feature vector, and setting the parameter values of texture feature and the number of iteration to segmentation of high-resolution remote sensing images. The improved mean shift algorithm was used to divide remote sensing images and compare the segmentation results of recognition software. The experimental results show that the improved algorithm can get higher segmentation precision and better segmentation results.