EVALUATION OF A META-TRANSFER APPROACH FOR FEW-SHOT REMOTE SENSING SCENE CLASSIFICATION

Large numbers of labeled data are necessary for the success of modern deep-learning techniques. Despite a large amount of satellite image data now available, their ground truth labels are inadequate due to the complexity of the real world. It is true for many remote sensing tasks including scene classification, target classification, and target detection. This study explores and evaluates a state-of-the-art pipeline that combines transfer learning and meta-learning methods with voluminous external data for few-shot remote sensing scene classification. The experimental findings demonstrate that using this pipeline, both in-domain and out-of-domain data can lead to equivalent performance as the base data during training. Additionally, the study explores the impact of various N-way-Kshot tasks in the meta-training stage and finds that the model trained with 5-way-5-shot tasks achieves the highest level of performance.