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