A SAR Target Recognition Algorithm Based on Guided Filter Reconstruction and Denoising Sparse Autoencoder

被引：0
|
作者：
Wang J. [1 ,2 ]
Qin C. [1 ]
Yang K. [1 ]
Ren P. [1 ]
机构：
[1] School of Electronics and Information, Northwestern Polytechnical University, Xi'an
[2] No.365 Institute, Northwestern Polytechnical University, Xi'an
来源：
Binggong Xuebao/Acta Armamentarii | 2020年 / 41卷 / 09期
关键词：
Denoising sparse autoencoder; Guided filter reconstruction; Regularized Softmax; Synthetic aperture radar; Target recognition;
D O I：
10.3969/j.issn.1000-1093.2020.09.018
中图分类号：
学科分类号：
摘要：
The existing synthetic aperture radar(SAR) target recognition algorithms have the poor generalization ability and high complexity. For the problems above, an algorithm based on the guided filter reconstruction and denoising sparse autoencoder is proposed. The guided filter reconstruction algorithm with two-scale image fusion preprocessing of SAR image is used to generate an one-dimensional image vector and normalizate it in order to reduce the dimension of output feature of the image and increase the speed of preprocessing. The algorithm would extract and recognize the low-dimensional features of images by reducing the hidden layer neurons of the denoising autoencoder, which can effectively reduce the complexity of the algorithm. The Softmax classifier is used for classifying. The experimental results show that the SAR target recognition algorithm based on the guided filter reconstruction and denoising sparse autoencoder can not only improve the target recognition performance and generalization ability, but also reduce the number of hidden layer neurons in the autoencoder and the computational complexity, and the network structure has also been improved and optimized as well. © 2020, Editorial Board of Acta Armamentarii. All right reserved.
引用
收藏
页码：1861 / 1870
页数：9
相关论文
共 17 条
  • [1] FENG Q C, PENG D L, GU Y., SAR target recognition with variants based on convolutional neural network, Journal of Image and Graphics, 24, 2, pp. 258-268, (2019)
  • [2] CHEN S, WANG H, XU F, Et al., Target classification using the deep convolutional networks for SAR images, IEEE Transactions on Geoscience and Remote Sensing, 54, 8, pp. 4806-4817, (2016)
  • [3] XU S P, ZHANG G Z, LI C X, Et al., A fast random-valued impulse noise detection algorithm based on deep belief network, Journal of Electronics & Information Technology, 41, 5, pp. 1130-1136, (2019)
  • [4] DENG S, DU L, LI C, Et al., SAR automatic target recognition based on Euclidean distance restricted autoencoder, IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 99, pp. 1-11, (2017)
  • [5] KANG M, JI K, LENG X, Et al., Synthetic aperture radar target recognition with feature fusion based on a stacked autoencoder, Sensors, 17, 1, (2017)
  • [6] DING J, LIU H W, CHEN B, Et al., Similarity constrained deep belief networks with application to SAR image target recognition, Journal of Electronics & Information Technology, 38, 1, pp. 97-103, (2016)
  • [7] CUI Z Y., Theory and key techniques research on synthetic aperture radar target recognition, (2015)
  • [8] XU J., Research on SAR image target recognition via compressed sensing, (2013)
  • [9] YU M T, DONG G G, FAN H Y, Et al., SAR target recognition via local sparse representation of multi-manifold regularized low-rank approximation, Remote Sensing, 10, 2, (2018)
  • [10] YANG L, SU J, LI X., Application of SAR ship data augmentation based on generative adversarial network in improved SSD, Acta Armamentarii, 40, 12, pp. 2488-2496, (2019)
12