Multispectral optical signatures of micro UAV: acquisition and database for usage in a neural network

Daniel Fitz; Ivo Buske; Andreas Walther; Juan Acosta

doi:10.1117/12.2636145

28 October 2022 Multispectral optical signatures of micro UAV: acquisition and database for usage in a neural network

Daniel Fitz, Ivo Buske, Andreas Walther, Juan Acosta

Proceedings Volume 12276, Artificial Intelligence and Machine Learning in Defense Applications IV; 122760I (2022) https://doi.org/10.1117/12.2636145
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

Small unmanned aerial vehicles (UAVs) are becoming more and more popular and also a challenge for civilian and military security. A UAV has to be detected first, but due to environmental condition (e.g. night or fog) the detection is impeded. To assess the threat of a possible hostile UAV, identification is helpful. If the type of UAV can be determined, information about size, payload, velocity and range can be given and countermeasures can be considered. Identification of UAVs can be more accurate using multiple spectral ranges at the same time. We present a systematic approach for acquisition of multispectral signatures in the field and in the lab, structured storage in a database and composition of partially synthetic images as training data for identification in an artificial neural network. We set up a multispectral camera system comprised of three imagers, in the visible spectrum, SWIR and MWIR. The cameras are externally triggered. This allows an image acquisition in the field with a synchronized video stream. In addition to that, high resolution images are made in the lab from different angles all around the micro UAV. A specific background is chosen, so it will be masked and with a given real world background image a partially synthetic image can be generated. These can be validated with data that was gathered in the field. Both are stored in a database, along with metadata, to allow access to particular data when needed. Synthetic images and signatures from the field can be used as multispectral training data for an artificial neural network to enable identification of a UAV.

Conference Presentation

Citation Download Citation

Daniel Fitz, Ivo Buske, Andreas Walther, and Juan Acosta "Multispectral optical signatures of micro UAV: acquisition and database for usage in a neural network", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 122760I (28 October 2022); https://doi.org/10.1117/12.2636145

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