Novel batch active learning approach and its application to synthetic aperture radar datasets

James Chapman; Bohan Chen; Zheng Tan; Jeff Calder; Kevin Miller; Andrea L. Bertozzi

doi:10.1117/12.2662393

13 June 2023 Novel batch active learning approach and its application to synthetic aperture radar datasets

James Chapman, Bohan Chen, Zheng Tan, Jeff Calder, Kevin Miller, Andrea L. Bertozzi

Proceedings Volume 12520, Algorithms for Synthetic Aperture Radar Imagery XXX; 125200B (2023) https://doi.org/10.1117/12.2662393
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

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 classifiers performance. Recent gains have been made using sequential active learning for synthetic aperture radar (SAR) data.1 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.

Conference Presentation

Citation Download Citation

James Chapman, Bohan Chen, Zheng Tan, Jeff Calder, Kevin Miller, and Andrea L. Bertozzi "Novel batch active learning approach and its application to synthetic aperture radar datasets", Proc. SPIE 12520, Algorithms for Synthetic Aperture Radar Imagery XXX, 125200B (13 June 2023); https://doi.org/10.1117/12.2662393

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