With the decreasing noise level of underwater vehicle, the infrared imaging characteristics of underwater vehicle wake become one of its main detectable sources. Using the infrared characteristics of underwater vehicle wake to remote sensing detect the traces of underwater vehicle has gradually developed into a new detection method. Because of the high contingency and large error in judging underwater vehicle wake artificially, it can be overcome by using deep transfer learning to identify and locate the wake. This paper focuses on the infrared feature recognition of underwater vehicle wake with deep transfer learning, and wake sample sets of different classes are produced by image classification. The training effect of different pre-training networks is compared by using deep transfer learning. The influence of internal parameters of pre-training networks on the training effect of wake is discussed. Finally, combined with Faster-RCNN algorithm, the identification effect of wake is tested. The final recognition accuracy is ideal. It has certain application potential for future research on wake remote sensing detection combined with convolution neural network identification.
A numerical method is proposed for the transport of infrared radiation in participating medium. The method is implemented using the Finite Volume Method (FVM) for solving the radiative transfer equation (RTE), and Mie theory for computing the absorption and scattering characteristics of the medium. The advantages of the method reflected in two aspects. On the one hand, the radiative characteristics is got from a data base established in advance using Mie theory, on the other hand, the scattering phase function is simplified by distinguishing the "forward average scattering" and "other directional average scattering". Both the two procedures yield significant computational savings with little loss in accuracy for predictions of spectral and total transmission.
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.