## 4 Results and Analysis
### SNR Estimation on Blind Imagery  
The experiment was considered from two aspects: (1) a comparison between estimated SNRs and calibrated SNRs in a laboratory, and (2) a spectral response curve trend, in particular, several spectral bands with strong absorption and reflection. Blind images were real remote sensing images without noise filtering and downsampling, etc. The SDC dedicated to estimating hyperspectral SNRs and the Net-1 method were selected as the reference methods. An L1A level [30] hyperspectral with 128 bands was selected as the test data, which is shown in Figure 1a. The SNRs of the hyperspectral sensor were calibrated before the aerial photography. Most of the hyperspectral bands had SNRs greater than 30 dB, and the average SNR was 40 dB. The SNR equation is described as follows:  
\[SNR=\frac{E}{\sigma} \tag{6}\]  
\[SNR_{dB}=20\log_{10}SNR, \tag{7}\]  
where \(E\) is the mean gray value of single band image, \(\sigma\) is the noise standard deviation, and \(SNR_{\text{dB}}\) is the decibel intensity of \(SNR\).  
Figure 9 shows the estimated SNR results. The different colors correspond to the three different methods: SDC (blue), Net-1 (orange), and Net-2-M (red). The average SNRs evaluated by the three methods are 29.8 dB, 34.5 dB, and 37.8 dB, respectively. Combined with the calibrated SNRs, with a total average of 40 dB, the average accuracy of Net-1 and Net-2-M are better than the SDC in Figure 9a, of which Net-2-M performs best (5.5% relative error on average). Combined with the analysis of the average gray level of each spectral band (shown in Figure 9b), the gray values of the first ten and the last ten bands are relatively smaller than others. Then, the SNRs should show an upward trend at the beginning and a downward trend at the end. In this case, all of the three methods show the same trend. Due to the unique spectrum absorption of chlorophyll in green vegetation near the 450 nm and 650 nm [31] (band 4 and 12 in Figure 9) wavelengths, the signals are weaker than the adjacent bands, which causes the SNRs to be smaller than the adjacent bands. Corresponding to this case, SDC and Net-2-M display similar trends, but Net-1 shows the difference. In addition, there is a \"red edge\" effect near 700 nm-950 nm (band 16-36) [32], and the signal strengths increase up sharply. Thus, the SNRs show an upward trend. Both SDC and Net-2-M follow these characteristics on the SNR curve. Meanwhile, the Net-1 curve is relatively flat and gentle. Since the water in plant tissues absorbs sunlight at the wavelengths of 1450 nm and 1190 nm [33] (band 76 and 112), the signals are also weak. Both of the SDC and Net-2-M curves show the situation. In summary, the SNR curve obtained by Net-2-M is consistent with the trend in the specific spectral response curve.  
The three methods are compared and analyzed in terms of the average accuracy and SNR trends at the specific spectrum response bands: (1) Net-2-M performs the best in accuracy (37.5 dB closest to the calibrated 40 dB on average); (2) Net-1 performs well on overall trends, but its local accuracy is insufficient. It is necessary to either increase the training epochs or deepen the neural network such as in Net-2; and (3) SDC is stable, but the average accuracy is lower than the other two methods.