Parallel Multiclass Support Vector Machine for Remote Sensing Data Classification on Multicore and Many-Core Architectures

Support Vector Machine (SVM) is a classification method that has been widely used in the domain of remote sensing for decades. Although SVM-based classification method achieves good performance for classification accuracy in many studies, it can become very time-consuming in some remote sensing applications such as hyperspectral image classification or large-scale land cover mapping. To improve the efficiency for SVM training and classification in remote sensing applications, we designed and implemented a highly efficient multiclass support vector machine (MMSVM) for x 86-based multicore and many-core architectures such as the Ivy Bridge CPUs and the Intel Xeon Phi coprocessor (MIC) based on our previous MIC-SVM library. Various analysis methods and optimization strategies are employed to fully utilize the multilevel parallelism of our studied architectures. We select several real-world remote sensing datasets to evaluate the performance of our proposed MMSVM. Compared with the widely used serial LIBSVM, our MMSVM achieves 6.3-31.1 x (in training) and 4.9-32.2 x(in classification) speedups on MIC, and 6.9-14.9x (in training) and 5.5-22.1x (in classification) speedups on the Ivy Bridge CPUs. We also conduct a performance comparison analysis on different platforms and provide some ideas on how to select the most suitable architecture for specific re-mote sensing classification problems in order to achieve the best performance.