Fast and automatic forest volume estimation based on K nearest neighbor and SAR

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.