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 remote sensing classification problems in order to achieve the best performance.