基于改进边缘分割算法的幼苗信息提取  

Extraction of Seedlings Information Based on Improved Edge Segmentation Algorithm

作　　者：李伟涛[1,2] 彭道黎[1] 吴见[2] 

机构地区：[1]北京林业大学省部共建森林培育与保护教育部重点实验室,北京100083 [2]滁州学院地理信息与旅游学院,滁州239000

出　　处：《农业机械学报》2014年第4期259-263,303,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家科技基础性工作专项资助项目(2013FY111600-1);'十一五'国家科技支撑计划资助项目(2006BAD23B05);安徽高等学校省级自然科学研究资助项目(KJ2013B189);滁州学院校级科研启动基金资助项目(2012qd18)

摘　　要：优化特征空间和改进分割算法是利用面向对象技术准确获取幼苗信息的关键,也是高空间分辨率数据提取目标地物信息迫切需要解决的问题。研究了在多光谱影像进行去噪声处理基础上,采用改进的基于边缘的算法进行影像分割,同时选取纹理、形状、光谱特征构建特征空间,实现幼苗信息提取的方法。结果表明,该方法对幼苗信息提取的总精度达86%,比传统技术提高了12%,KAPPA系数达0.814 5,比传统技术提高了0.115 9。该方法可以对幼苗信息进行准确快速提取,能够为生产或管理部门进行准确监测和决策提供依据,对未来造林情况进行预测和评价有重要意义。Optimization of feature space and improvement of the segmentation algorithm are the keys of accurately obtaining seedling information using object-oriented technology.An improved edge segmentation algorithm was used to segment image based on dealing with the noise of multispectral images.The algorithm developed the simulated balloon expansion method,and could control the direction of the force field,so that the curves were made to split and collapse inwards.And the feature space made up of texture,shape,spectral features was built to accomplish seedling information extraction.The results showed that the total accuracy of seedling information extraction was 86% by the method of this paper,12% higher than that of traditional methods,and the KAPPA coefficient was 0.814 5,0.115 9 higher than that of traditional methods.The method of this paper could accomplish seedling information extraction quickly and accurately,and provide a reference for the accurately monitoring and decision making to management departmen.It has important meaning to forecast and evaluation for the future afforestation situation.

关 键 词：幼苗 改进边缘分割算法 多光谱 影像分割 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]