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Proceedings Paper

Fast and automatic forest volume estimation based on K nearest neighbor and SAR
Author(s): Ying Guo; Zeng-yuan Li; Er-xue Chen; Xu Zhang
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Paper Abstract

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.

Paper Details

Date Published: 24 October 2011
PDF: 6 pages
Proc. SPIE 8286, International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications, 82861D (24 October 2011); doi: 10.1117/12.912332
Show Author Affiliations
Ying Guo, Chinese Academy of Forestry (China)
Zeng-yuan Li, Chinese Academy of Forestry (China)
Er-xue Chen, Chinese Academy of Forestry (China)
Xu Zhang, Chinese Academy of Forestry (China)


Published in SPIE Proceedings Vol. 8286:
International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications
Jonathan Li, Editor(s)

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