小麦冠层图像H分量的K均值聚类分割

K-means clustering segmentation for H weight of wheat canopy image

大田环境下小麦冠层图像具有光照不均匀、背景复杂及阴影遮挡等特点,经典图像分割算法存在精度低、过分割等问题,提出一种基于HSI空间下H分量的K均值聚类算法。使用R+G-B归一化处理RGB空间下的彩色图像,以抑制其B分量;将归一化图像进行RGB到HSI的颜色空间转化;根据光照是否均匀,使用K均值聚类算法对彩色图像的H分量进行不同的聚类处理,经形态学开运算及去噪处理获得最终目标图像。实验表明,该方法对不同施氮量、不同光照、不同生长时期小麦冠层图像的分割效果较好,相对基于Lab空间的K-means聚类分割,该方法可一定程度避免过分割现象;相对基于H分量的Otsu算法,对光照不均匀图像分割更完整,对复杂背景图像分割更精确。

Wheat canopy image under the natural light has the feature of nonuniform illumination, complicated background with shadows. A K-means clustering algorithm based on HSI color space is proposed to conquer the problem of low accuracy and over segmentation existing in classic image segmentation algorithm. To restrain B weight, R＋G-B is used to nor-malize color images in RGB space. After transforming the normalized image from RGB to HSI color space, the different methods of K-means cluster used to the H weight depend on whether the sunlight is uniform or not. The final image is gained after using mathematical morphology and noise-removal process. The experiments show that compared with K-means cluster processing in Lab space and Otsu algorithm based on H weight, the method based on H weight can avoid over seg-mentation and has accurate segmentation results in different N fertilization, different illumination and different periods.