CAJ | 학술논문

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。
대전배경하소맥관층도상구유광조불균균、배경복잡급음영차당등특점，경전도상분할산법존재정도저、과분할등문제，제출일충기우HSI공간하H분량적K균치취류산법。사용R+G-B귀일화처리RGB공간하적채색도상，이억제기B분량；장귀일화도상진행RGB도HSI적안색공간전화；근거광조시부균균，사용K균치취류산법대채색도상적H분량진행불동적취류처리，경형태학개운산급거조처리획득최종목표도상。실험표명，해방법대불동시담량、불동광조、불동생장시기소맥관층도상적분할효과교호，상대기우Lab공간적K-means취류분할，해방법가일정정도피면과분할현상；상대기우H분량적Otsu산법，대광조불균균도상분할경완정，대복잡배경도상분할경정학。
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.