一种深度神经网络SAR遮挡目标识别方法

doi:10.3969/j.issn.1001-2400.2015.03.026

Abstract

Abstract:

For synthetic aperture radar(SAR) partial occluded images feature extraction and target recognition, a new method based on deep neural networks(DNN) is proposed. After preprocessing original images, we extract low-frequency sub-band images of SAR images in the wavelet domain as training data, and finally make a further extraction of the occluded targets' feature with the deep sparse autoencoder model as the input vectors. Three types of target in MSTAR database are used to simulate target occlusion and recognition experiment. Experimental results prove that the correct recognition rate could be improved by taking advantage of both local and global information about occluded targets, and that the new method is effective for feature extraction and object recognition of occluded SAR targets.

Key words: synthetic aperture radar, target recognition, occluded targets, deep learning, deep sparse autoencoders

LI Shuai;XU Yuelei;MA Shiping;NI Jiacheng;SHI Hehuan. New method for SAR occluded targets recognition using DNN[J].J4, 2015, 42(3): 154-160.

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New method for SAR occluded targets recognition using DNN

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