Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands

Remote Sensing

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