###### Abstract  
Signal-to-Noise Ratio (SNR) is the benchmark to evaluate the quality of optical remote sensors. For SNR estimation, most of the traditional methods have complicated processes, low efficiency, and general accuracy. In particular, they are not suitable for the distributed computation on intelligent satellites. Therefore, an intelligent SNR estimation algorithm with strong computing power and more accuracy is urgently needed. Considering the simplicity of distributed deployment and the lightweight goal, our first proposition is to design a convolutional neural network (CNN) similar to VGG (proposed by Visual Geometry Group) to estimate SNR for optical remote sensors. In addition, considering the advantages of multi-branch structures, the second proposition is to train the CNN in a novel method of multi-branch training and parameter-reconstructed inference. In this study, simulated and real remote sensing images with different ground features are utilized to validate the effectiveness of our model and the novel training method. The experimental results show that the novel training method enhances the fitting ability of the network, and the proposed CNN trained in this method has high accuracy and reliable SNR estimation, which achieves a 3.9% RMSE for noise-level-known simulated images. When compared to the accuracy of the reference methods, such as the traditional and typical SNR methods and the denoising convolutional neural network (DrCNN), the performance of the proposed CNN trained in a novel method is the best, which achieves a relative error of 5.5% for hyperspectral images. The study is fit for optical remote sensing images with complicated ground surfaces and different noise levels captured by different optical remote sensors. quality estimation; signal-to-noise ratio; CNN; multi branch training; parameter reconstruction 2023 20  
how to decline the influence of heterogeneous information is one of the critical steps for traditional algorithms. Many scholars have performed many studies on noise estimation. Their achievements have enriched and developed the relevant algorithms. These traditional methods are divided into three categories. The first is manually selecting the homogeneous areas or making the geo-statistics. For example, the studies by [PERSON] et al. and [PERSON] and [PERSON] et al. [4] manually select narrow strips of homogeneous areas and then estimate the noise standard deviations (SDs) of these strips. This is a smart idea, but it does not work well for images with irregular homogeneous areas. Ref. [5] proposed a geostatistical method in which the nugget variance is calculated and fitted. This algorithm results in large noise deviation. [PERSON]. [6] proposed a nonparametric algorithm based on eigenvalues in polynomial time to estimate the noise level. However, the principle of the algorithm is complicated. The second is automatically extracting the homogeneous areas by removing the edges of adjacently different ground objects. Local standard deviation (LSD) [7] is a classic method considering heterogeneous information. LSD improves the accuracy of SNR estimation and is one of the early widely applied algorithms. [PERSON] [8] improved LSD's accuracy of noise estimation by removing edges. However, the accuracy highly depends on how many edges can be removed [9]. Ref. [10] is different from the traditional rectangle block division. Firstly, it analyzes the local information to realize multi angle block division. Secondly, the number of homogeneous sample blocks depends on the threshold in fractal theory. This method accuracy is greatly affected by the threshold. Thus, an appropriate threshold is also one of the keys of the method. The third utilizes the spectrum or spatial correlation. For example, [PERSON] et al. [11] proposed a spectral decorrelation (SDC) method that considers that the adjacent spectrum is correlated, and noise is random in the spectrum dimension. SDC is applied to assess the SNRs of hyperspectral remote sensing images and achieves great accuracy.