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Active learning improves the performance of machine learning methods byjudiciously selecting a limited number of unlabeled data points to query forlabels, with the aim of maximally improving the underlying classifier'sperformance. Recent gains have been made using sequential active learning forsynthetic aperture radar (SAR) data arXiv:2204.00005. In each iteration,sequential active learning selects a query set of size one while batch activelearning selects a query set of multiple datapoints. While batch activelearning methods exhibit greater efficiency, the challenge lies in maintainingmodel accuracy relative to sequential active learning methods. We developed anovel, two-part approach for batch active learning: Dijkstra's Annulus Core-Set(DAC) for core-set generation and LocalMax for batch sampling. The batch activelearning process that combines DAC and LocalMax achieves nearly identicalaccuracy as sequential active learning but is more efficient, proportional tothe batch size. As an application, a pipeline is built based on transferlearning feature embedding, graph learning, DAC, and LocalMax to classify theFUSAR-Ship and OpenSARShip datasets. Our pipeline outperforms thestate-of-the-art CNN-based methods.

Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets