Currently, deep neural networks have been widely used in the field of SAR target recognition. Many researchers found that deep neural networks have an ability of denoising. In many cases, there is no need to denoise i...
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ISBN:
(纸本)9781665403696
Currently, deep neural networks have been widely used in the field of SAR target recognition. Many researchers found that deep neural networks have an ability of denoising. In many cases, there is no need to denoise in pre-process. But the denoising ability of deep neural networks can take place of conventional denoisingalgorithm or not is doubtful. In this article, we explore the effect of image denoisingalgorithms to SAR target recognition methods based on deep neural networks. Firstly, seven traditional denoisingalgorithms are selected to process two SAR datasets. And these data are utilized to train two kinds of deep neural networks. After comparing and analyzing the training processes and results, we find that 1) The effect of denoisingalgorithms is influenced by architectures of neural networks and quality of datasets. It is difficult to find a SAR image denoisingalgorithm, which can improve the accuracy of any recognition network. Sometimes they even drag down the performance of recognition networks. 2) The deep networks with more layers will have better denoising ability, so the effect of denoisingalgorithms will decrease. For ResNet, there is no need to add the denoising processing.
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