Multisource Image Classification Based on Parallel Minimum Classification Error Learning

Abstract

In this paper we present a parallel classification learning method, referred to as parallel minimum classification error (PMCE) learning, for supervised classification of multisource remote sensing images. The approach is based on the positive Boolean function (PBF) classifier scheme. The PBF implements the minimum classification error (MCE) as a criterion to improve classification performance. By evenly distributing both positive and negative samples of MCE learning modules to different PMCE learning nodes, PMCE outperforms the original one in terms of execution time. It fully utilizes the significant parallelism embedded in MCE learning of PBF to create a set of PMCE learning nodes implemented by using the message passing interface (MPI) library and the open multi-processing (OpenMP) application programming interface. A sophisticated hierarchical structure of hybrid PMCE, which combines cluster based MPI with multicore-based OpenMP, is proposed to demonstrate the flexibility of implementation of the proposed scheme. The effectiveness of the proposed PMCE is evaluated by fusing MODIS/ASTER airborne simulator (MASTER) hyperspectral images and the Airborne Synthetic Aperture Radar (AIRSAR) images for land cover classification during the Pacrim II campaign. The experimental results demonstrated that PMCE can improve the computational speed of PBF classification significantly.