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

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/support-vector-machine-versus-random-forest-for-remote-sensing-image/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Mohammadreza Sheykhmousa,Masoud Mahdianpari,Hamid Ghanbari,Fariba Mohammadimanesh,Pedram Ghamisi,Saeid Homayouni |
| Citations | 1,073 |
| DOI | 10.1109/jstars.2020.3026724 |
| Fields | Earth and Planetary Sciences,Engineering,Environmental Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://ieeexplore.ieee.org/ielx7/4609443/8994817/09206124.pdf |
| OpenAlex ID | https://openalex.org/W3088162569 |
| Type | review |
| Year | 2020 |

## Paper authors

- [Pedram Ghamisi](https://scholariq.org/researchers/pedram-ghamisi/)

## Paper primary topic

- [Remote-Sensing Image Classification](https://scholariq.org/topics/remote-sensing-image-classification/)

## Paper topics

- [Remote-Sensing Image Classification](https://scholariq.org/topics/remote-sensing-image-classification/)
- [Remote Sensing in Agriculture](https://scholariq.org/topics/remote-sensing-in-agriculture/)
- [Remote Sensing and Land Use](https://scholariq.org/topics/remote-sensing-and-land-use/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.