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Article

Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning

1
Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea
2
Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea
3
Agency for Defense Development, 111 Sunam-dong, Daejeon 34186, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 72; https://doi.org/10.3390/rs10010072
Received: 14 November 2017 / Revised: 15 December 2017 / Accepted: 3 January 2018 / Published: 11 January 2018
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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 ( α ) can measure the offline sensor confidence per target category based on the classification rate for an evaluation set. The second weight ( β ) 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 ( α β -sum) and neural network-based nonlinear fusion scheme ( α β -NN). The experimental results confirmed the proposed linear fusion method ( α β -sum) to have the best performance among the linear fusion schemes available (SAR-CNN, IR-CNN, α -sum, β -sum, α β -sum, and Bayesian fusion). In addition, the proposed nonlinear fusion method ( α β -NN) showed superior target recognition performance to linear fusion on the OKTAL-SE-based synthetic database. View Full-Text
Keywords: SAR; IR; fusion; double weights; linear; nonlinear; deep learning; OKTAL-SE SAR; IR; fusion; double weights; linear; nonlinear; deep learning; OKTAL-SE
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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. https://doi.org/10.3390/rs10010072

AMA 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 Sensing. 2018; 10(1):72. https://doi.org/10.3390/rs10010072

Chicago/Turabian Style

Kim, Sungho, Woo-Jin Song, and So-Hyun Kim. 2018. "Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning" Remote Sensing 10, no. 1: 72. https://doi.org/10.3390/rs10010072

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