5 Key Metrics to Evaluate Few-Shot Remote Sensing Models

Few-shot remote sensing tasks require tailored metrics like F1 score, Kappa, and PR curves to properly evaluate model performance on imbalanced data.

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5 Evaluation metrics for few-shot remote sensing task

Before delving into the various approaches in a few-shot remote sensing task, we highlight in this section some evaluation metrics that are more suited for few-shot learning task. The data distribution would display some degree of imbalance between the training set and the test set for small-sample size unlike typical learning-based tasks, and hence appropriate metrics addressing such imbalance would need to be invoked. We illustrate in Table 1 the various metrics along with a brief overview. The metrics are the confusion matrix, precision, recall, F1 score, Overall Accuracy (OA), Average Accuracy (AA), Kappa coefficient κ, and PR curve. (5)-(9) mathematically describe some of the metrics as indicated in the respective ...