Evaluating active learning methods for synthetic aperture radar maritime object detection

Jonathan Sato; Julian Raheema; Martin Jaszewski

doi:10.1117/12.2636037

28 October 2022 Evaluating active learning methods for synthetic aperture radar maritime object detection

Jonathan Sato, Julian Raheema, Martin Jaszewski

Proceedings Volume 12276, Artificial Intelligence and Machine Learning in Defense Applications IV; 1227609 (2022) https://doi.org/10.1117/12.2636037
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

Maritime object detection in synthetic aperture radar (SAR) imagery has seen a resurgence of interest due to the introduction of deep object detection models and large-scale datasets from competitions such as xView3. However, as novel examples are seen in the wild and as new SAR sensors emerge, existing models will need to be retrained with relevant new data. Active learning (AL) aims to automate and optimize this data curation process. In this work, we evaluate state-of-the-art AL algorithms for the task of SAR maritime object detection. Through analyzing and identifying gaps in current AL solutions we seek to motivate efforts to improve their utility in practical settings.

Conference Presentation

Citation Download Citation

Jonathan Sato, Julian Raheema, and Martin Jaszewski "Evaluating active learning methods for synthetic aperture radar maritime object detection", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 1227609 (28 October 2022); https://doi.org/10.1117/12.2636037

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available