In few-shot remote sensing tasks, appropriate evaluation metrics are critical due to the inherent imbalance between training and testing datasets. The article discusses specific metrics that enhance the efficacy of evaluation in such contexts. Key metrics include precision, recall, F1 score, Overall Accuracy, Average Accuracy, and the Kappa coefficient, which take into account the limited data points available per category. The nuances of these metrics emphasize the need for their careful application to ensure accurate assessment of learning models in remote sensing applications.
In few-shot learning settings, the evaluation metrics must reflect the data imbalance commonly observed, containing largely fewer samples per class leading to skewed results; thus, specialized metrics are vital.
Appropriate evaluation metrics for few-shot remote sensing tasks include the confusion matrix, precision, recall, F1 score, Overall Accuracy, and Kappa coefficient, specifically formulated to cater to class imbalances.
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