CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12506 > Article
Open Access Paper
28 December 2022 Classification method for spatial targets based on convolutional neural network
Tianwei Yang, Yu Pei, Chaowei Li, Dongshan Cheng
Author Affiliations +
Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 125060E (2022) https://doi.org/10.1117/12.2662743
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China
Abstract
In this paper, we constructed a one-dimensional convolutional neural network as a classifier model for spatial object classification. Considering that there are few available training samples obtained from actual measurement, combining with the characteristics of actual measurement data, we simulated a large amount of data for training and testing. The simulation results show that our method has a high classification accuracy and can overcome the problems existing in actual measurement, such as tracking mixed batches to a certain extent, and it can also effectively solve the problem that it is difficult to directly train neural networks because of the small number of spatial target samples, which take advantage of neural network autonomous learning and memory to reliably identify features.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Tianwei Yang, Yu Pei, Chaowei Li, and Dongshan Cheng "Classification method for spatial targets based on convolutional neural network", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 125060E (28 December 2022); https://doi.org/10.1117/12.2662743
PROCEEDINGS
 5 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Rockets
Satellites
Target recognition
Radar
Convolutional neural networks
Convolution
Neural networks
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top