## 5 Discussion  
In this study, we contributed two important aspects: (1) The design of a lightweight CNN for SNR estimation; (2) The proposal of a novel training and parameter-reconstructed inference method to enhance traditional CNNs that are similar to VGG. The experimental results prove the effectiveness of our work. 1. In this study, [20] and the proposed models (Net-1 and Net-2 in Figure 4) are compared. The two CNNs are trained in the traditional method. Net-2 has one more FC layer and one more BN layer than Net-1. When comparing the results of the two CNNs on the dataset, the deeper network (Net-2) performs better. Regardless of the gradient vanishing, the appropriate deepening of a neural network would extract more abstract and useful feature information [34,35]. The useful information transmitting could reduce the redundant and interfering information to a certain degree. 2. In the comparative experiment, there is another analyzable test: a comparison between traditional training and multi-branch training. Figure 4 shows that the multi-branch training method is more accurate than the traditional training method. The reasons are: (a) multiple branches extract more useful features than one branch and achieve the effect of a deeper network; and (b) during the backward propagation, the upstream gradients are passed to each branch, and the gradient attenuation of each branch is the same. It accelerates the gradient updating and makes the whole training process converge quickly, which reduces the training time. For example, the training epochs of Net-1-M are less than Net-1, but the average RMSE obtained by Net-1-M is minimal; the RMSE obtained by Net-1-M is close to Net-2, but Net-1-M has less layers than Net-2. 3. For the accuracy tested on the noise-known images, five methods are used as reference methods. The dataset contains different scenes such as farmland, roads, city, sea, mountain and vegetation, etc. When compared with each method, the proposed method is still better than others. The traditional methods are limited by heterogeneous or complicated land surfaces [9,12]. For the denoising DnCNN, the residual noise tends to contain part of the image structure information, which causes the high error of noise estimation (shown in Figure 7). Unlike denoising DnCNN, our model learned the feature of Gaussian noise rather than the ground features. For different land surfaces, our model still performed well. Figure 9: The SNR results estimated on the hyperspectral imagery by SDC, Net-1, and Net-2-M. The green dash line represents the SNRs calibrated in laboratory (most of SNRs >30 dB and were 40 dB on average). The figure also shows the absorption and reflection of solar spectrum by green vegetation: spectral absorption of chlorophyll at the wavelength 450 nm and 650 nm (band 4 and 12); “red edge” near 700–950 nm (band 16–36); spectral absorption of water in plant tissue at 1450 nm and 1900 nm (band 76 and 112). (**a**) The SNR results estimated by three methods; (**b**) The average gray value of each band. In the test on blind imagery, three methods (SDC, Net-1, and Net-2-M) are used to estimate SNR on real hyperspectral imagery with 128 bands captured by a UAV flight. The SNRs of the remote sensor are calibrated in the laboratory before aerial photography: 40 dB on average for 128 bands. Compared with the calibrated SNRs, the Net-1 and Net-2-M are more accurate than SDC, which is restricted to the complicated ground features. For optical remote sensors, thermal noise hardly changes with signals [36] under stable conditions. In other words, thermal noise is affected by temperature changing and hardly affected by spectrum frequency. Then, at the absorption or reflection bands, the SNRs are lower or higher than those adjacent bands. In this case, the SNR curve obtained by Net-2-M is consistent with the specific spectral response compared with the other two methods. 5. The performance is not ideal for the proposed method in this case as the noise intensity increases, and the RMSE also increases (shown in Table 4).