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Correction: Liu, B., et al. Quantitative Evaluation of Pulsed Thermography, Lock-In Thermography and Vibrothermography on Foreign Object Defect (FOD) in CFRP. Sensors 2016, 16, doi:10.3390/s16050743
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Sensors 2017, 17(1), 192; doi:10.3390/s17010192

Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder

1
School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China
2
Beijing Institute of Remote Sensing Information, Beijing 100192, China
*
Author to whom correspondence should be addressed.
Academic Editors: Cheng Wang, Julian Smit, Ayman F. Habib and Michael Ying Yang
Received: 19 October 2016 / Revised: 20 December 2016 / Accepted: 13 January 2017 / Published: 20 January 2017
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [2942 KB, uploaded 20 January 2017]   |  

Abstract

Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm. View Full-Text
Keywords: SAR target recognition; feature fusion; stacked autoencoder; MSTAR SAR target recognition; feature fusion; stacked autoencoder; MSTAR
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Kang, M.; Ji, K.; Leng, X.; Xing, X.; Zou, H. Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder. Sensors 2017, 17, 192.

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