文献类型：会议论文

英文题名：Fast and automatic forest volume estimation based on K nearest neighbor and SAR

作者：Guo Ying[1] Li Zeng-yuan[1] Chen Er-xue[1] Zhang Xu[1]

第一作者：郭颖

通信作者：Guo, Y[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

会议论文集：International Symposium on Lidar and Radar Mapping - Technologies and Applications

会议日期：MAY 26-29, 2011

会议地点：Nanjing, PEOPLES R CHINA

语种：英文

外文关键词：KNN; parallel computing; SAR; forest volume; leave-one-out; cross-validation

年份：2011

摘要：In the recent years, the estimation of forest volume using radar data has developed greatly. However, as the radar data was large scale, the efficiency of processing based on KNN decreased seriously. Moreover, because the different K and distance measured method could result in the different accuracy, the treatment could have a low degree of automation under the condition of keeping the relatively better precision. Therefore, the study implemented a tool which could have the feature of fast and automatic processing radar data based on KNN. For enhancing the efficiency of processing, the tool was implemented in the way of parallelization by using the message passing interface (MPI) technology and run on the high performance cluster environment. To certain the suitable parameter automatically such as K and the appropriate distance measured method during the processing; the study used leave-one-out cross-validation method to check the precision and selected the optimum model based on the accuracy. The result shows that the tool accelerated the computation speed as eight time as before while ensuring the treatment precision and improved the automatic degree of the treatment. To some extend, it solved the bottleneck of processing large scale SAR data.