The study of parallel KNN in the identification of forest type based on multi-spectral data

Forest type identification is one of most important contents of forest inventory. K nearest-neighbour algorithm has already proven their use in forest mapping. However, as the remote sensing data was large scale, the efficiency of processing based on KNN decreased seriously. Therefore, the study implemented a tool which could have the feature of fast processing multi-spectral 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. By segmenting the input large scale image in some small block and parallel processing all these block, the computing time was shorten greatly. 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 remote sensing data.