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Open AccessArticle

Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

1
College of Communication Engineering, Chongqing University, Chongqing 400044, China
2
Department of Communication Commanding, Chongqing Communication Institute, Chongqing 400035, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Sensors 2016, 16(9), 1413; https://doi.org/10.3390/s16091413
Received: 29 June 2016 / Revised: 17 August 2016 / Accepted: 29 August 2016 / Published: 2 September 2016
(This article belongs to the Section Physical Sensors)
Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS) sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with 1 -regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR) database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance. View Full-Text
Keywords: microwave imaging sensor; image classification; aspect angle; sparse representation microwave imaging sensor; image classification; aspect angle; sparse representation
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MDPI and ACS Style

Zhang, X.; Yang, Q.; Liu, M.; Jia, Y.; Liu, S.; Li, G. Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification. Sensors 2016, 16, 1413.

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