Paper Info
Title
Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification.
Abstract
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 l(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.
Year
DOI
Venue
2016
10.3390/s16091413
SENSORS
Keywords
Field
DocType
microwave imaging sensor,image classification,aspect angle,sparse representation
Least squares,Computer vision,Microwave,Target acquisition,Aspect angle,Pattern recognition,Computer science,Synthetic aperture radar,Sparse approximation,Artificial intelligence,Recognition algorithm,Contextual image classification
Journal
Volume
Issue
ISSN
16
9
1424-8220
Citations 
PageRank 
References 
0
0.34
7
Authors
6
Name
Order
Citations
PageRank
Xinzheng Zhang133.77
Qiuyue Yang200.34
Miaomiao Liu300.34
Yunjian Jia46713.92
Shujun Liu514.46
Guo-jun Li6105.28