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

doi:10.3969/j.issn.1000-1093.2020.09.018

Abstract

Abstract: 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.

Key words: syntheticapertureradar, guidedfilterreconstruction, denoisingsparseautoencoder, regularizedSoftmax, targetrecognition

CLC Number:

TN959.1⁺7

WANG Jian， QIN Chunxia， YANG Ke， REN Ping. A SAR Target Recognition Algorithm Based on Guided Filter Reconstruction and Denoising Sparse Autoencoder[J]. Acta Armamentarii, 2020, 41(9): 1861-1870.

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