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Rejection methods for vegetation mapping using hyperspectral airborne data
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DOI：10.1080/01431161.2023.2240520.png)
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摘要
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Vegetation mapping from remote sensing data has proven useful for monitoring ecosystems at local, regional and global scales. Generally based on supervised classification methods, ecosystem mapping needs representative and consistent labelling. Such completeness is often difficult to achieve and requires the exclusion of minority species poorly represented in the studied scene in the training base. This exclusion leads to wrong predictions in the resulting map. In this study, the use of a posteriori classification rejection methods to limit the errors associated with minority species was evaluated in three different mapping scenarios: classification according to vegetation layers, prediction of genera from various vegetation types from low vegetation to trees and mapping of habitat (assemblages of species). For this purpose, several supervised classification methods based on Support Vector Machines (SVM), Random Forests (RF) and Regularized Logistic Regression (RLR) algorithms were first applied to hyperspectral images covering the reflective domain. On these classifications, the usual evaluation methods (confusion matrix and its derivatives calculated on an independent test set composed of the majority species) showed performances similar to those of the state-of-the-art. However, the introduction of a new score taking into account minority species demonstrated the need to include them in the evaluation process to provide robust performance quantification representing map effectiveness. Three a posteriori rejection methods, based on simple thresholding, K-means and SVM algorithms, were well suited to monitor minority species. The performance gain depended on the mapping scenario, the machine learning model and the rejection method. An increase in performance with the inclusion of minority species of up to 12% could be observed through the new score. These methods also detected a similar proportion of prediction errors associated with predominant species
Keyword:
Supervised classification
rejection method
hyperspectral
vegetation mapping
machine learning
minority species
期刊
IF:
2.6
论文数:
1.2W
被引数:
2.7W
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