Classifying objects in LWIR imagery via CNNs

Iain Rodger; Barry Connor; Neil M. Robertson

doi:10.1117/12.2241858

21 October 2016 Classifying objects in LWIR imagery via CNNs

Iain Rodger, Barry Connor, Neil M. Robertson

Proceedings Volume 9987, Electro-Optical and Infrared Systems: Technology and Applications XIII; 99870H (2016) https://doi.org/10.1117/12.2241858
Event: SPIE Security + Defence, 2016, Edinburgh, United Kingdom

Abstract

The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave Infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.

Citation Download Citation

Iain Rodger, Barry Connor, and Neil M. Robertson "Classifying objects in LWIR imagery via CNNs", Proc. SPIE 9987, Electro-Optical and Infrared Systems: Technology and Applications XIII, 99870H (21 October 2016); https://doi.org/10.1117/12.2241858

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