顾及超像素光谱特征的无人机影像自动模糊聚类分割法EI北大核心CSCD

针对现有流行模糊C均值聚类在影像分割中存在边界依附能力弱,分割不稳定及需要手动设置聚类数目等问题,提出一种顾及超像素光谱特征的无人机影像自动模糊聚类分割方法。相对于传统分水岭变换算法,该方法首先采用两步边界推进准则,生成轮廓更加精确、形状规则更加紧凑的超像素子区域;然后,提取子区域光谱特征并结合重缩放密度峰值算法自动获取聚类数目;最后,综合利用超像素光谱特征与隐式马尔可夫随机场思想对模糊聚类进行改进,实现超像素精确合并。通过两组影像数据的定性分析和定量评价表明,本文方法能准确定位目标边界,获得较好的视觉分割结果,同时有效提高了影像分割精度。

Superpixel spectral features-based automatic fuzzy clustering segmentation for UAV image

Aiming at the problems of the existing popular fuzzy C-means clustering in image segmentation, such as the weak boundary attachment ability, the unstable segmentation process and the need to manually set the number of clusters, a super-pixel spectral features-based automatic fuzzy clustering segmentation for UAV image is proposed. Firstly, the watershed-based super-pixels algorithm with boundary advancing criterions are used to generate boundary adherent and compact super-pixels. Then extract the spectral features of super-pixels, and obtain the cluster number is automatically by rescaled density peak algorithm. Finally, an improved FCM method combining spectral features and hidden Markov random field is adopt to achieve high-precision super-pixels merging. Through qualitative analysis and quantitative evaluation, the results show that the proposed method can accurately locate the target boundary, obtain the optimal segmentation results and effectively improve the image segmentation accuracy.