###### Abstract  
The low-frequency line spectrum of the radiated noise signals of hydroacoustic targets contains features describing the intrinsic properties of the target that make the target susceptible to exposure. In order to extract the line spectral features of underwater acoustic targets, a method combining image processing and a deep autoencoder network (DAE) is proposed in this paper to enhance the low-frequency weak line spectrum of underwater targets in an extremely low signal-to-noise ratio environment based on the measured data of large underwater vehicles. A Gauss-Bernoulli restricted Boltzmann machine (G-BRBM) for real-value signal processing was designed and programmed by introducing a greedy algorithm. On this basis, the encoding and decoding mechanism of the DAE network was used to eliminate interference from environmental noise. The weak line spectrum features were effectively enhanced and extracted under an extremely low signal-to-noise ratio of 10-300 Hz, after which the reconstruction results of the line spectrum features were obtained. Data from large underwater vehicles detected by far-field sonar arrays were processed and the results show that the method proposed in this paper was able to adaptively enhance the line spectrum in a data-driven manner  
deep learning theory's emergence, scholars have designed many deep neural networks for feature extraction. From 2015 to 2020, [PERSON] et al. [7] used DNNs to learn features from wavelet packet component energy that are more advanced and discriminative than spectral features extracted by traditional methods. [PERSON] [8] applied multiple depth structures to SAR feature extraction and obtained excellent experimental results. [PERSON] et al. [9] patched occluded images in a real environment by fusing the out-of-field features of the occluded region with in-field features to form an optimized map of the incomplete image, which could then be used for feature extraction and for the prediction of partially occluded regions of the complete image. However, this network model was only applied to the original image and does not address the denoising of global noise. The stacked convolutional sparse denoising autoencoder (SCSDA) model proposed by [PERSON] et al. [10] combines a sparse autoencoder and convolutional neural network to extract the depth features of water sound signals with impressive denoising performance. [PERSON] et al. [11] used the convolutional neural network and denoising auto encoder network (CDAE) for end-to-end infrared small target detection, which is more efficient and accurate than traditional methods; additionally, the structural loss they proposed helps to preserve background texture features during the encoding process, which can carry out unsupervised extraction of certain features. The underwater denoising autoencoder (UDAE) model, proposed by [PERSON] et al. [12], integrates the self-encoder with the convolutional layer to extract invariant features from underwater images and to enhance their clarity. The deep convolutional denoising autoencoder proposed by [PERSON] et al. [13] can remove the noise from received signals quickly and efficiently and can also perform well at low SNRs, but the method requires active sound and its covertness is low. In the last three years, [PERSON] et al. [14] have used a LOFAR-CNN deep neural network, [PERSON] et al. [15] have enhanced hydroacoustic images using the denoising autoencoder, and [PERSON] et al. [16] have used an improved autoencoder network with stacking, sparsity, and denoising functions for DEMON spectral feature extraction. [PERSON] et al. [17; 18] used neural networks to extract different levels of shallow features, reconstructing an image using the idea of multilevel feature fusion, and they also reduced the number of parameters with an asymmetric residual module to finally obtain a super-resolution reconstructed image.