农田图像绿色植物自适应分割方法

为了适应不同农田地块自然图像颜色特征固有的差异性，从土壤背景中准确分割出各种绿色植物目标，设计了一种自适应分割方法。首先利用初始有标签训练样本获得支持向量机分割模型，然后根据K均值聚类算法自动从待分割图像中提取无标签训练样本，与有标签训练样本组成混合训练样本集。而后在混合样本集基础上，使用直推式支持向量机训练方法得到新的有标签训练样本集，挖掘出待分割图像颜色特征的分布信息。最后，利用这些新的有标签样本在线自适应更新分割模型。农田图像试验结果显示，该方法可以提高分割模型的针对性，有效地增强了分割过程适应农田自然图像颜色特征差异性的能力。

Adaptive segmentation of field image for green plants

Abstract: There are inherent variations of color features in images of different natural field environments, which lead to a big challenge when areas of all kinds of green plants need to be extracted from soil background according to the color information in some applications, such as weeds recognition and agricultural robot navigation. So an adaptive image segmentation method was developed. Firstly, the initial segmentation model was set up based on support vector machine (SVM) with the labeled training samples. Then the unlabeled training samples were obtained automatically from the images to be segmented based on the K-means clustering. Both the unlabeled and labeled samples constituted the set of hybrid training samples. Secondly, the transductive support vector machine (TSVM) was trained based on these hybrid training samples. The unlabeled samples were labeled by the TSVM, and the distribution information of color features in the images which provided unlabeled samples was mined out. Finally, the new set of labeled training samples was obtained by discarding some inappropriate ones in the hybrid training samples according to their distance to the optimal classification plane. The initial segmentation model was updated with new labeled training samples, and in this way the updated model was more pertinent to the particular field images for segmentation. The experimental results showed that the pertinence between the segmentation model and the specific natural field image had been improved, and the adaptability of segmentation processing had been enhanced significantly.