Remote Sensing | |
Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands | |
Chris B. Joyce1  Miguel Villoslada Peciña2  Thaisa F. Bergamo2  Kalev Sepp2  Ricardo Martínez Prentice2  Raymond D. Ward2  | |
[1] Centre for Aquatic Environments, School of the Environment and Technology, University of Brighton, Cockcroft Building, Moulsecoomb, Brighton BN2 4GJ, UK;Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia; | |
关键词: UAV; machine learning; Random Forest; KNN; classification; comparison; | |
DOI : 10.3390/rs13183669 | |
来源: DOAJ |
【 摘 要 】
High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs better than K-Nearest Neighbors (KNN) algorithm in the classification of pixels and objects and the classification based on pixel analysis is slightly better than the object-based analysis. The agreement between the classifications of objects and pixels is higher in Random Forest. This is likely due to the heterogeneity of the study areas, where pixel-based classifications are most appropriate. In addition, from an ecological perspective, as these wetlands are heterogeneous, the pixel-based classification reflects a more realistic interpretation of plant community distribution.
【 授权许可】
Unknown