# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 4 Conclusions  
Remote sensing technology is gradually replacing manual field surveys as high-resolution data are applied to aquaculture at multiple levels. China's ground observation system acquires high-resolution data in an attempt to modify conventional optical remote sensing technology. This study used GF-1, GF-2, and WV-2 remote sensing data to perform PCA, GS, and NNDiffuse fusion for images from enclosure seine and floating raft culture areas. Quantitative evaluations with seven statistic indexes and \(CQ_{max}\) were performed separately for the different data sources and aquiculture areas with qualitative evaluations. The experimental analysis shows that the fusion processed images are clearer in spatial structure than the original image and provide more information after fusion, which illustrates the importance of fusion. Various methods can improve the spatial resolution of the original image but can also cause changes to the spectral information of the original image. The application background for this study is aquaculture information extraction, especially enclosure seine culture areas and floating raft culture areas. In addition to water, aquaculture facilities on the water are the targets to be identified, even a single culture raft. The distance between the features is close and the distribution is dense, so the boundary feature extraction is very important. This study not only uses statistical quantitative methods but also the \(CQ_{max}\) index and visual interpretation to evaluate the fusion image, comparing and analyzing the evaluation results of the three image fusion methods. Visual interpretation is more intuitive and simpler but lacks data support, whereas the statistical method can comprehensively evaluate the impact after the fusion (seven statistical indicators introduced). The statistical evaluation is objective and accurate, but its calculation and process are cumbersome. The \(CQ_{max}\) index is applied to image evaluation. Although the calculation process is simple, the evaluation results are not based on specific indicators but are instead directly obtained by program algorithms [29]. The study results show that the conclusion of the \(CQ_{max}\) evaluation and the evaluation results of statistical parameters are not completely consistent. As shown in Table 8, according to the quantitative evaluation results of seven statistic indexes for the enclosure seine culture area, the optimal fusion methodology varied with the data type. GS fusion produced the best results for GF-1 images, and PCA fusion was optimal for GF-2 and WV-2 data sources. In the background of floating raft culture areas, NNDiffuse produced the best fusion results for GF-1 and GF-2 data sources, and PCA  
Figure 11: \(CQ_{max}\) of fusion results in the floating raft culture area. fusion was optimal for integrating WV-2 data. The \(CQ_{max}\) metric was used to compare image quality for three multispectral data sources fused with varying modalities [29]. For the enclosure seine culture area, the results showed PCA to be optimal for GF-1 data. NNDiffuse was optimal for GF-2 and WV-2 data, with little variation in the GF-2 results. For the floating raft culture area, NNDiffuse was also optimal for GF-1 and GF-2 data, and PCA was optimal for WV-2 data, which is consistent with the results of qualitative and subjective evaluations. Therefore, we can see that the two conclusions of quantitative evaluations are different for the enclosure seine culture area, while for the floating raft culture area, the two conclusions are the same. In terms of improving the spatial resolution of the image, PCA and NNDiffuse perform better in spatial detail enhancement, which has a high degree of image definition and information. The GS fusion method is not very effective and should not be used as a basic image for classification. Note that no matter what kind of remote sensing data source, the image will be distorted to a certain degree after fusion.