Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping
| Autor: | Anna Tardà, Jordi Corbera, Bogdan Zagajewski, Marcin Kluczek, Anca Dabija, Ahmed H. Al-Sulttani, Marlena Kycko, Lydia Pineda, Edwin Raczko |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Support Vector Machine
Catalonia 010504 meteorology & atmospheric sciences Computer science Science 0211 other engineering and technologies 02 engineering and technology Land cover computer.software_genre 01 natural sciences Deforestation Classifier (linguistics) media_common.cataloged_instance land cover mapping Corine Random Forest Braila Warsaw European union Accuracy class 021101 geological & geomatics engineering 0105 earth and related environmental sciences media_common Pixel 15. Life on land Random forest Support vector machine General Earth and Planetary Sciences Data mining computer |
| Zdroj: | Remote Sensing; Volume 13; Issue 4; Pages: 777 Remote Sensing, Vol 13, Iss 777, p 777 (2021) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13040777 |
| Popis: | Land cover information is essential in European Union spatial management, particularly that of invasive species, natural habitats, urbanization, and deforestation; therefore, the need for accurate and objective data and tools is critical. For this purpose, the European Union’s flagship program, the Corine Land Cover (CLC), was created. Intensive works are currently being carried out to prepare a new version of CLC+ by 2024. The geographical, climatic, and economic diversity of the European Union raises the challenge to verify various test areas’ methods and algorithms. Based on the Corine program’s precise guidelines, Sentinel-2 and Landsat 8 satellite images were tested to assess classification accuracy and regional and spatial development in three varied areas of Catalonia, Poland, and Romania. The method is dependent on two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM). The bias of classifications was reduced using an iterative of randomized training, test, and verification pixels. The ease of the implementation of the used algorithms makes reproducing the results possible and comparable. The results show that an SVM with a radial kernel is the best classifier, followed by RF. The high accuracy classes that can be updated and classes that should be redefined are specified. The methodology’s potential can be used by developers of CLC+ products as a guideline for algorithms, sensors, and the possibilities and difficulties of classifying different CLC classes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |