文献类型：会议论文

英文题名：Tree crown recognition algorithm on high spatial resolution remote sensing imagery

作者：Deng, Guang[1] Li, Zengyuan[1] Wu, Honggan[1]

第一作者：邓广

通信作者：Li, Z.

机构：[1] Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China

会议论文集：Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

会议日期：16 October 2010 through 18 October 2010

会议地点：Yantai

语种：英文

外文关键词：Artificial intelligence - Errors - Forestry - Image resolution - Image segmentation - Shape optimization

年份：2010

摘要：To extract information at the individual tree level ,which is very useful in biologyinverted commas ecology and forestry, would be prohibitively time-consuming and be necessary for artificial intelligence by considering many factors. The presented approach develops a tree top seeded based region growth tree detection and crown delineation algorithm for analyzing QuickBird satellite images in Populusxxiaohei plantation even stand at Xue Jia Zhuang wood farm in Shanxi Province of China. After multi resolution segmentation, we get image object segments for tree top seeds detection with NDVI and ratio NIR feature. Around theses seeds, we let them region growing in a cycle way. Some false seeds must be wiped off with given feature threshold. After quad tree segmentation for crown shape optimization, the same category region must be merged. We use 9 plots with different plantation density to validate the above method. Average tree numbers identification error is 18.9% , R2 = 0.4693. From comparing tree numbers of field work and software identification by tree matching, the confusion matrix, overall accuracy, commission error, omission error is computed. ?2010 IEEE.