Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning
Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea
Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea
Agency for Defense Development, 111 Sunam-dong, Daejeon 34186, Korea
Author to whom correspondence should be addressed.
Received: 14 November 2017 / Revised: 15 December 2017 / Accepted: 3 January 2018 / Published: 11 January 2018
This paper presents a novel double weight-based synthetic aperture radar (SAR) and infrared (IR) sensor fusion method (DW-SIF) for automatic ground target recognition (ATR). IR-based ATR can provide accurate recognition because of its high image resolution but it is affected by the weather conditions. On the other hand, SAR-based ATR shows a low recognition rate due to the noisy low resolution but can provide consistent performance regardless of the weather conditions. The fusion of an active sensor (SAR) and a passive sensor (IR) can lead to upgraded performance. This paper proposes a doubly weighted neural network fusion scheme at the decision level. The first weight (
) can measure the offline sensor confidence per target category based on the classification rate for an evaluation set. The second weight (
) can measure the online sensor reliability based on the score distribution for a test target image. The LeNet architecture-based deep convolution network (14 layers) is used as an individual classifier. Doubly weighted sensor scores are fused by two types of fusion schemes, such as the sum-based linear fusion scheme (
-sum) and neural network-based nonlinear fusion scheme (
-NN). The experimental results confirmed the proposed linear fusion method (
-sum) to have the best performance among the linear fusion schemes available (SAR-CNN, IR-CNN,
-sum, and Bayesian fusion). In addition, the proposed nonlinear fusion method (
-NN) showed superior target recognition performance to linear fusion on the OKTAL-SE-based synthetic database.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Scifeed alert for new publications
Never miss any articles
matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
Define your Scifeed now
Share & Cite This Article
MDPI and ACS Style
Kim, S.; Song, W.-J.; Kim, S.-H. Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning. Remote Sens. 2018, 10, 72.
Kim S, Song W-J, Kim S-H. Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning. Remote Sensing. 2018; 10(1):72.
Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun. 2018. "Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning." Remote Sens. 10, no. 1: 72.
Show more citation formats
Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
[Return to top]
For more information on the journal statistics, click here
Multiple requests from the same IP address are counted as one view.