Vessel classification for naval operations

Richard J. M. den Hollander; Sabina B. van Rooij; Sebastiaan P. van den Broek; Judith Dijk

doi:10.1117/12.2599513

13 September 2021 Vessel classification for naval operations

Richard J. M. den Hollander, Sabina B. van Rooij, Sebastiaan P. van den Broek, Judith Dijk

Proceedings Volume 11870, Artificial Intelligence and Machine Learning in Defense Applications III; 118700N (2021) https://doi.org/10.1117/12.2599513
Event: SPIE Security + Defence, 2021, Online Only

Abstract

An important surveillance task during naval military operations is early risk assessment of vessels. The potential risk that a vessel poses will depend on the vessel type, and vessel classification is therefore a basic technique in risk assessment. Although automatic identification by AIS is widely available, the AIS transponders can potentially be spoofed or disabled to prevent identification. A possible complementary approach is the use of automatic classification based on camera imagery. The dominant approach for visual object classification is the use of deep neural networks (DNNs), which has shown to give unparalleled performance when sufficiently large annotated training data sets are available. However, within the scenario of naval operations there are several challenges that need to be addressed. First, the number and types of classes should be defined in such a way that they are relevant for risk assessment while allowing sufficiently large training sets per class type. Second, early risk assessment in real-life conditions is vital and vessel type classification should work on long range target imagery having low-resolution and being potentially degraded. In this paper, we investigate the performance of DNNs for vessel classification under the aforementioned challenges. We evaluate different class groupings for the MARVEL vessel data set, both from an accuracy perspective and the relevancy for risk assessment. Furthermore, we investigate the impact of real-life conditions on classification by manually downsizing and reducing contrast of the MARVEL imagery, as well as evaluating on EO/IR recordings from Rotterdam harbor which has been collected for several weeks under varying weather conditions.

Conference Presentation

Citation Download Citation

Richard J. M. den Hollander, Sabina B. van Rooij, Sebastiaan P. van den Broek, and Judith Dijk "Vessel classification for naval operations", Proc. SPIE 11870, Artificial Intelligence and Machine Learning in Defense Applications III, 118700N (13 September 2021); https://doi.org/10.1117/12.2599513

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