Support vector machine parallelized remote sensing image classification algorithm based on big data

With the development of big data technology, machine learning classification methods have been widely used in the classification and recognition of remote sensing images. For remote sensing big data, how to quickly and efficiently use machine learning classification algorithms to classify remote sensing images is an urgent problem. It is a general term for the theory, method, technology, and activities of obtaining valuable information based on massive remote sensing data sets, synthesizing auxiliary data from other sources, and using big data thinking and means. The purpose of this paper is to study the support vector machine (SVM) parallelized remote sensing image classification algorithm based on big data. We propose a parallel nesting of GPU in MPI multiprocesses based on the big data framework, which can more effectively improve the calculation processing speed and build a high-performance SVM parallel computing framework based on the big data framework. The optimization problem of SVM considers both empirical risk and structural risk minimization and requires maximum edge distance when constructing hyperplane decision boundaries, so there is ample space between the interval boundaries to accommodate the test samples. Based on this framework, we improve the machine learning SVM algorithm and realize the high-performance parallel computing of SVM classification algorithm on this platform. It is an efficient hybrid parallel mode to nest GPU in MPI multiprocess in parallel. When the number of nodes is 2, 4, and 6, the speedup of the SVM classification algorithm is 1.52, 2.24, and 2.55. (c) 2022 SPIE and IS&T

Automated summary

This paper studies the support vector machine (SVM) parallelized remote sensing image classification algorithm based on big data. A method of nesting GPU in MPI multiprocesses within the big data framework is proposed to effectively improve the calculation processing speed and build a high-performance SVM parallel computing framework. The SVM algorithm is optimized by considering both empirical risk and structural risk minimization, and by constructing hyperplane decision boundaries with maximum edge distance. Experimental results show that the SVM classification algorithm achieves speedups of up to 2.55 with different numbers of nodes.