姿态图像缺失情况下的SAR目标识别

doi:10.3969/j.issn.1001-2400.2016.04.002

Abstract

Abstract:

The performance of synthetic aperture radar (SAR) image target recognition depends on the diversity of pose images in the training set. The problem of lack of pose images is considered, and the method of training data augmented with the synthesized pose images is introduced to train the classifier for target identification. Inspired by the sparse representation model, the model for synthesizing pose images is also developed, which approximately construct the missing pose image by linearly combining several images available. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method of pose images synthesis can increase the recognition accuracy effectively. In particular, significant improvement can be obtained in the case of serious lack of pose images.

Key words: synthetic aperture radar (SAR) image target recognition, lack of pose images, sparse representation

DING Jun;LIU Hongwei;CHEN Bo;WANG Yinghua. SAR image target recognition in lack of pose images[J].Journal of Xidian University, 2016, 43(4): 5-9.

References

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SAR image target recognition in lack of pose images

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