关键词: SAR目标识别, 型号识别, 稀疏编码树, 字典学习, 稀疏表示

Abstract: In order to improve the SAR (synthetic aperture radar) target recognition performance, especially for variant target recognition performance, this paper proposes a sparse coding tree framework for radar target recognition based on the sparse representation recognition method. Sparse coding tree is a classification tree that uses node-specific dictionaries and classifiers at each node. In the training stage, this paper designs two types of classifiers respectively for target identification requirements and variant target recognition requirement. The root node is built to identify target type, while child nodes of the tree are built to identify variant target series. In the testing stages, the sparse coding tree recognizes the target type on the root node. Then according to the recognition result of the root node, the tree decides whether branching the test sample to a child node or outputting final recognition result. Target with variant type will branch to the child node trained for this specific target type group. In addition, target without a variant will simply output a final classification label. The experimental results on MSTAR (moving and stationary target acquisition and recognition) SAR image data sets show that the sparse coding tree improves the ability of variant target  recognition, while obtains similar target category recognition accuracy compared to the mainstream method.

Key words: SAR target recognition, variant target recognition；sparse coding tree, dictionary learning；sparse representation