*3.4. Digital Comparison*

To quantitatively measure the proposed method, three image quality metrics, i.e., mean-square-error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) [40], are selected to evaluate the translation effectiveness. MSE measures the difference between the real and simulated values. The smaller the MSE is, the more similar the two images are. PSNR is a traditional image quality evaluation index. A higher PSNR generally indicates a higher image quality. SSIM measures the structural similarity between the real image and the simulated image.

The test dataset used in the visual comparison is also taken for quantitative comparison. The dataset includes 500 remote sensing images. The average values of the evaluation metrics are calculated, and the results are listed in Tables 4 and 5, where optimal values are highlighted in bold.

**Table 4.** Quality result of the translation from infrared spectrum domain to visible spectrum domain.


**Table 5.** Quality result of the translation from visible spectrum domain to infrared spectrum domain.


The results of Tables 4 and 5 show that the proposed SDTGAN method is superior to other comparative methods. It achieves better image recognizable structure and data authenticity in the spectral domain translation from infrared spectrum domain to visible spectrum domain and vice versa.
