Automatic Target Classification " Experiments on the MSTAR SAR Images

Authors:
Yinan Yang;Yuxia Qiu;Chao Lu
Affiliations:
Towson University;Towson University;Towson University
Venue:
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
Year:
2005

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Abstract

SAR (Synthetic Aperture Radar) can produce target images in range and cross-range with sufficient resolution for recognition. In this paper, we did an experimental test on three different feature extraction techniques (Principle Components Analysis PCA, Independent Components Analysis ICA, and Hu moments) by using different target SAR images taken from the MSTAR database. The performance of these techniques is analyzed. A number of classification techniques, such as Linear (LDC), Quadratic (QDC), K-nearest Neighbor (K-NN), and Support Vector Machine (SVM) are tested and compared for their performance on the target classification. Our experimental results provide a guideline for selecting feature extracting techniques and classifiers in automatic target recognition using SAR image data.