KEYWORDS: Visualization, Unmanned aerial vehicles, Sensors, Data fusion, System identification, Neural networks, Defense and security, Data acquisition, Classification systems
Object identification or classification has found many applications, ranging from civilian to defense application scenarios and there is a rising need for a both effective and efficient identification approach. One of the common methods for this task is using a neural network. However, it could be very difficult for such a network to obtain accurate answers due to complex environments, especially when the data is of single modality. In this paper, we attempt to build a combined deep learning model which takes two distinct data modalities to help us achieve high accuracy multimodal classification systems. An experiment is conducted on Multimodal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance (AU-AIR) using both visual and sensor data. We compare our results between a model trained with visual data only and another combined model trained with both visual and sensor data. Improved object classification performance is observed when the multimodal method is applied.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.