# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Evaluation Methodology
#### 3.1.1 GF-1 Fusion Evaluation  
Fused GF-1 images (Figure 4) from the enclosed aquaculture area were assessed using seven indicators (Appendix B). The amount of information in the entropy values is mainly used to observe the ability to maintain details before and after image fusion. The information entropy in an image quantifies the richness and diversity of data in the form of feature statistics. Table 2 shows that the information entropy in the GS fusion images was higher than in the original multispectral data, indicating that the information contained in the GS fusion image is richer. In contrast, the entropy of the PCA and NNDiffuse results is zero. This is likely because the GF-1 grayscale values and range are too large and the bands are too interdependent.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**GF-1** & **Band** & \begin{tabular}{c} **Gray Mean** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **Information** \\ **Entropy** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Gradient** \\ \end{tabular} & \begin{tabular}{c} **Correlation** \\ **Coefficient** \\ \end{tabular} & \
\begin{tabular}{c} **Spectral** \\ **Distortion** \\ \end{tabular} &
\begin{tabular}{c} **Bias Index** \\ \end{tabular} \\ \hline \multirow{4}{*}{MS} & Band1 & 241.31 & 27.52 & 6.29 & 6.30 & 0 & 0 & 0 \\  & Band2 & 240.13 & 33.84 & 6.67 & 9.77 & 0 & 0 & 0 \\  & Band3 & 170.81 & 42.01 & 6.88  
Standard deviation was used to evaluate grayscale divergence in the images. As seen in Table 2, the standard deviation of PCA and NNDiffuse results was similar to that of the original data, producing a single image level. In contrast, the GS fusion images are colorful, contain more information, and exhibit a larger standard deviation. The texture in the image is clear, the features of the ground are obvious, and the imaging effect is excellent. The average gradient of each band in the GS images is larger than in the original fusion data. The gray level difference is also large in Band 4 and the information is diverse. Based on this, it can be applied to image feature extraction or color synthesis. The grayscale mean and standard deviation followed a similar pattern, with larger gray levels in the GS images producing richer image levels.  
Spectral fidelity is primarily used to compare original multi-spectral images with fused results. This study investigated the effects of bias index and the degree of spectral distortion present in the fused data. Table 2 shows that the bias index and spectral distortion of the fused images are much larger than the original multispectral data, with varying degrees of spectral distortion present in each band. It is evident that spectral distortion is higher because the fusion will cause spectral distortion, and the image fidelity will be affected, but the degree of influence is different. This value is smaller in B1, B3 (GS fusion), and B4 (PCA fusion), indicating these bands preserved spectral fidelity. The bias index was smaller in B1, B2, and B3 (GS fusion) than with other fusion techniques, indicating high spectral retention. Correlation coefficients in the B3 (GS) and B4 (PCA) bands were large, indicating image information was uniform after image fusion. This coefficient was negative for the GS and NNDiffuse techniques in B2, but other bands showed a higher correlation. The gray mean value in B4 was particularly high, suggesting the image was disturbed by noise during phase formation. In general, GS fusion is optimal for GF-1 data.