Artículos de revistas
Land-Cover Classification Using MaxEnt: Can We Trust in Model Quality Metrics for Estimating Classification Accuracy?
Fecha
2020-03Registro en:
Morales, N. S., & Fernández, I. C. (2020). Land-Cover Classification Using MaxEnt: Can We Trust in Model Quality Metrics for Estimating Classification Accuracy?. Entropy, 22(3), 342.
1099-4300
WOS: 000526524300002
PMID: 33286116
10.3390/e22030342
Autor
Morales, Narkis S. [Univ Mayor, Fac Ciencias, Ctr Modelac & Monitoreo Ecosistemas, Chile]
Fernández, Ignacio C. [Univ Mayor, Fac Ciencias, Ctr Modelac & Monitoreo Ecosistemas, Chile]
Institución
Resumen
MaxEnt is a popular maximum entropy-based algorithm originally developed for modelling species distribution, but increasingly used for land-cover classification. In this article, we used MaxEnt as a single-class land-cover classification and explored if recommended procedures for generating high-quality species distribution models also apply for generating high-accuracy land-cover classification. We used remote sensing imagery and randomly selected ground-true points for four types of land covers (built, grass, deciduous, evergreen) to generate 1980 classification maps using MaxEnt. We calculated different accuracy discrimination and quality model metrics to determine if these metrics were suitable proxies for estimating the accuracy of land-cover classification outcomes. Correlation analysis between model quality metrics showed consistent patterns for the relationships between metrics, but not for all land-covers. Relationship between model quality metrics and land-cover classification accuracy were land-cover-dependent. While for built cover there was no consistent patterns of correlations for any quality metrics; for grass, evergreen and deciduous, there was a consistent association between quality metrics and classification accuracy. We recommend evaluating the accuracy of land-cover classification results by using proper discrimination accuracy coefficients (e.g., Kappa, Overall Accuracy), and not placing all the confidence in model's quality metrics as a reliable indicator of land-cover classification results.