基于数字照片特征的小麦覆盖度自动提取研究

Automated digital image analyses for estimating percent ground cover of winter wheat based on object features.

植被覆盖度是农作物的一个重要参数.本文利用数字相机获取田间小麦冠层影像,通过基于特征的自动影像分类估测覆盖度和叶面积指数.在分析数字影像中小麦和背景特征的基础上设计了小麦覆盖度的提取算法,并开发了一个对影像进行自动分类提取小麦覆盖度的程序包.该方法综合了ISO-DATA无监督分类的快速、操作简便的优点和最大似然法监督分类精度高的优点,分类精度达90%左右.此外还分析了自动分类的误差来源,提出了改进办法,指出了自动分类方法的适用范围.

Percent ground cover of vegetation is an important parameter for crop management. An innovational method based on features of objects was presented to automatically estimate percent ground cover of winter wheat from digital image analyses. Based on the features of wheat and its background components,an algorithm was designed to extract the percent ground cover, and the corresponding program was developed. This method was simple, labor- and time- saving with high classification accuracy about 90%. and the method combined the advantages of ISODATA method and maximum likelihood method. Finally the error source of automatic classification and scope of application were analyzed, and some approaches of improving accuracy of classification were discussed.