Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

TLDR

Remote sensing image classification has employed numerous machine‑learning algorithms over the past two decades, with Random Forest and Support Vector Machine emerging as the most widely studied methods. This review conducts a meta‑analysis of 251 peer‑reviewed papers to compare RF and SVM concepts and performance in remote sensing classification. The authors built a database of over 40 quantitative and qualitative fields from the selected studies and analyzed study characteristics and comparative classification performance across data types, applications, spatial resolution, and feature‑engineering parameters. The analysis identifies key challenges, offers recommendations, and outlines future research directions, providing a summary to help researchers tailor RF and SVM approaches for optimal accuracy in specific thematic applications.

Abstract

Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing applications. This article reviews RF and SVM concepts relevant to remote sensing image classification and applies a meta-analysis of 251 peer-reviewed journal papers. A database with more than 40 quantitative and qualitative fields was constructed from these reviewed papers. The meta-analysis mainly focuses on 1) the analysis regarding the general characteristics of the studies, such as geographical distribution, frequency of the papers considering time, journals, application domains, and remote sensing software packages used in the case studies, and 2) a comparative analysis regarding the performances of RF and SVM classification against various parameters, such as data type, RS applications, spatial resolution, and the number of extracted features in the feature engineering step. The challenges, recommendations, and potential directions for future research are also discussed in detail. Moreover, a summary of the results is provided to aid researchers to customize their efforts in order to achieve the most accurate results based on their thematic applications.