###### Abstract
FFT-DC [12] also addresses that random noise and signal are uncorrelated, and signals of the same ground objects are correlated in spatial dimensions. This feature separates noise from contaminated signals well, although the algorithm process is complicated and low in efficiency. [PERSON]. [13] proposed a new method (ReSNR) to calculate the column- and row-wise SNR for linear array push-broom imaging payloads. This method is suitable for homogeneous or uncomplicated textures. With the development of artificial intelligence, neural networks have been paid more and more attention and applied in almost every field. However, for noise estimation, neural networks are mainly used in denoising research. For example, the study by [PERSON] et al. [14] proposed a denoising algorithm based on deep convolution neural network (DnCNN), which performs more effectively than TNRD [15] for images with known and unknown noise levels. Since then, many denoising algorithms based on deep convolution and residual learning structures have emerged, such as [16; 17]. Although there are few studies on noise assessment (not denoising) based on neural networks, their studies also provide experiences and ideas for scholars. For example, the studies by [PERSON] et al., [PERSON] et al., and [PERSON] et al. [18] are the first to introduce artificial neural networks into noise evaluation. [PERSON]. [19] extracted the feature vectors of scene structures and noise, respectively. Following this, they are input into an artificial neural network to estimate SNR. This method obtains an average measuring error of less than 10% on its dataset. However, a large number of image parameters needs to be calculated in advance. [PERSON]. [20] proposed a lightweight convolutional neural network similar to VGG for noise assessment, which improved the efficiency on the premise of SNR assessment accuracy. In general, some common issues in SNR estimation are listed as below:  
* Complicated and low efficiency [8]: Traditional SNR algorithms depend on complicated physical or mathematical models to analyze textured and structural information. The complicated analytical and time-consuming computational methods are programmed, including methods such as multiple linear regression, covariance matrices, and Fourier transforms, etc. * General accuracy: Most of the methods are affected by the uneven distribution and complicated textures of ground surfaces. For example, Ref. [8] highly depends on whether edges can be removed. Additionally, Ref. [11] is applied in hyperspectral images but hardly works for multi-specworks and panchromatic images. * Lack of intelligent algorithms: Intelligent satellites need intelligent algorithms. However, most intelligent algorithms are mainly used in denoising research such as [15], etc. The denoising algorithms aim to improve image quality (restoring textures, refining details, and improving contrast). Thus, the noise residuals between the original and the restored images contain part of structural information of ground objects or part of the noise information [21], which causes the biased errors of noise estimation. At present, few neural networks are used to estimate noise levels in real-life applications. To overcome the problems, a convolutional neural network similar to VGG and a novel training method are proposed. In addition to the basic inference model structure, it also includes the novel training method. The training uses multi-branch structure. Multi-branch structure could obtain more useful information, which is proven by many famous models. However, it increases the quantity of weight parameters. Thus, when performing inference, we integrate the multi-branch into one branch, which keeps the quantity of weight parameters same as the original network. To summarize, our main contributions are the following:  
* The proposed CNN provides an intelligent method to estimate SNR directly. It is suitable for distributed deployment on intelligent satellites, which is a prominence compared with traditional methods.