###### Abstract
Similarly, although they show the excellent performance of neural networks for image feature extraction, the images themselves do not involve denoising and spectral problems. [PERSON] et al. [19] reviewed the application of neural networks in image restoration, the important part of which is the processing of image features by neural networks, and found that the merit of the features extracted by neural networks deeply affects the quality of the final image. In the denoising of hydroacoustic data, [PERSON] et al. [20] used 3D features combined with a CRNN network model to identify underwater acoustic targets and used data enhancement to improve the subsequent target identification rate; however, the neural network required a large number of known data labels to train the network, and its application in many scenarios may be limited. [PERSON] [21] used an autoencoder to extract features automatically, which allows for the selection of the optimal combination of features based on type and dimensionality without human intervention; in combination with recurrent and wavelet networks, the temporal features of spectrograms can be received and fixed. In many cases, supervised neural networks such as CNN [22; 23; 24] require the use of a gradient descent on a labeled training set to reduce training errors. To achieve the objective function, it has to have the following features:  
1. Labeled data is required;
2. Some papers use supervised learning methods to train the network. When it has fewer layers, usually converge the parameters can converge to a reasonable range. However, when this method is used to train deep networks, results are not satisfactory. In particular, training a neural network using a supervised learning approach usually involves solving a highly non-convex optimization problem, such as minimizing training error. For deep networks, the search region of such non-convex optimization problems is filled with a large number of local extremes, thus, using gradient descent does not work well;
3. As the depth of the network increases, the magnitude value of the backward propagation's gradient decreases. This results in a very small derivative of the loss function with respect to the weights of the initial layers. Thus, using gradient descent, the weights of the initial layers change so slowly that the network is not able to learn effectively from samples. To address the above problems, this paper uses the layer-by-layer greedy algorithm [25], whose main idea is to compute one layer of the obtained network at a time, which means a network containing one hidden layer is computed, and only when this layer is computed over, we start computing a network with two hidden layers. In each step, we fix the first k-1 layers that we have already obtained and then add the kth layer (i.e., we take as input the output of the first k-1 that we have already obtained). Then, during the computational acquisition of the model, we use an unsupervised method to initialize the weights of the deep network with the weights obtained from each layer individually. Finally, the entire network is \"fine-tuning\". Our model takes LOFAR spectrum as input, and only requires superposition of multiple layers of the autoencoder network, which is computationally simple, and allows us to extract deep line spectral features, and realize the denoising and enhancement effects of the spectrogram. Considering that the actual marine environment is very complex, the acquired underwater targets data are often heavily contaminated, which is caused by background noise. Under the environment of very low signal-to-noise ratio, it is difficult to fully express information of underwater targets with the classical two-valued restricted Boltzmann machine (RMB), as traditional methods cannot repair the broken and weak line spectrum. In this paper, a feature enhancement extraction method combining two-dimensional time and time-frequency spectrum (LOFAR spectrum) with deep autoencoder network (DAE) is proposed.