使用简单线性迭代聚类(SLIC)算法对遥感影像进行超像素分割时,存在运行时间长与边缘贴合度差的问题,因此,提出了一种基于改进SLIC的遥感图像超像素分割算法。首先,改进了初始种子点的初始化方式,消除了随机分配造成的影响;其次,在每次迭代后引入滤波操作,去除超像素内与聚类中心在颜色空间上差异较大的像素点,用剩余的像素点更新聚类中心;最后,用改进的均值计算公式进行迭代以实现超像素分割。在Python环境下的实验结果表明,在超像素个数相同的情况下,相比经典的SLIC算法,本算法在相同数据集中的分割误差率降低了7.4%、分割精度提高了1.4%,可在有效提高边缘轮廓贴合度的同时降低算法的计算复杂度。

When using simple linear iterative clustering (SLIC) algorithm for super-pixel segmentation of remote sensing images, there are problems of long running time and poor edge fitting. Therefore, a super-pixel segmentation algorithm of remote sensing image based on improved SLIC is proposed in this paper. First, the initialization method of initial seed points is improved to eliminate the influence of random distribution. Second, after each iteration, a filtering operation is introduced to remove pixels in the super-pixel that are significantly different from the clustering center in color space, and the clustering center is updated with the remaining pixel points. Finally, the super-pixel segmentation is realized by iteration with the improved mean value calculation formula. The experimental results in the Python environment show that in the case of the same number of super pixels, compared with classic SLIC algorithm, this algorithm reduces the segmentation error rate by 7.4%, improves the segmentation accuracy by 1.4%. It can effectively improve the fit of the edge contour and reduce the computational complexity of the algorithm.