# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas  
[PERSON]  
1 Key Laboratory of Fishery Resources Remote Sensing and Information Technology, Chinese Academy of Fishery Sciences, Shanghai 200090, China; [PERSON]] (X.W. ); [EMAIL_ADDRESS] (F.T. ); [EMAIL_ADDRESS] (J.L.) 1  
[PERSON]  
2 East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; [EMAIL_ADDRESS] 2  
[PERSON]  
1 Key Laboratory of Fishery Resources Remote Sensing and Information Technology, Chinese Academy of Fishery Sciences, Shanghai 200090, China; [PERSON]] (X.W. ); [EMAIL_ADDRESS] (F.T. ); [EMAIL_ADDRESS] (J.L.) 1  
[PERSON]  
1 Key Laboratory of Fishery Resources Remote Sensing and Information Technology, Chinese Academy of Fishery Sciences, Shanghai 200090, China; [PERSON]] (X.W. ); [EMAIL_ADDRESS] (F.T. ); [EMAIL_ADDRESS] (J.L.) 1  
[PERSON]  
1 Key Laboratory of Fishery Resources Remote Sensing and Information Technology, Chinese Academy of Fishery Sciences, Shanghai 200090, China; [PERSON]] (X.W. ); [EMAIL_ADDRESS] (F.T. ); [EMAIL_ADDRESS] (J.L.) 1  
Footnote 1: email: {jiasheng.liang. started as early as the late 1980s and early 1990s [3; 4; 5; 6]. Many fusion techniques have been developed [7; 8], such as PCA, GS, and NNDiffuse. With the development of these fusion methods, the problem of determining which method is the perfect one for specific data arises. How to effectively evaluate image fusion quality to provide convincing evaluation results is the key point when using image fusion products. In the IEEE GRSS 2006 Data Fusion Contest, the seven indexes of mean bias, variance difference, standard deviation difference, correlation coefficient, spectral angle mapper, relative dimensionless global error, and Q4 quality index were used [9]. [PERSON] [10] introduced a structural similarity variable under the framework of image quality assessment (the CQ index), which can effectively quantify the spatial correlation between two images. This metric was later used for elapsed AR-2D image similarity assessment. Then, [PERSON] et al. [11] proposed the target image fusion performance index (\(\mathit{CQ}_{\mathit{max}}\)) based on maximum co-divergence, which exhibited a high degree of similarity during subjective evaluation. However, this method is not specifically applied to the evaluation of image fusion results containing water information. [PERSON] et al. also used the \(\mathit{CQ}_{\mathit{max}}\) coefficient as an intermediate step to develop a recovery algorithm, generating an original image from two distorted images. These results suggest the \(\mathit{CQ}_{\mathit{max}}\) index outperformed the SSIM [12; 13] and \(\mathit{CQ}\) indices, without comparing the image fusion results with traditional statistical parameters. China is the world's largest producer of aquaculture. It accounts for a third of fish production, and two-thirds of the production of aquaculture worldwide [14]. In 2018, one-third of Chinese aquaculture production comes from saltwater [15]. However, aquaculture development faces major threats and challenges, such as the increasing competition for land, water, as well as water pollution, harmful algal blooms (HABs), and other threats [16; 17]. Due to its ability to map essential variables at multiple scales and resolutions, Earth observation (EO) can help to comprehensively optimize aquaculture location and type in both the nearshore and offshore oceans [18]. Xiangshan Bay is one of the important sites of salt aquaculture, which is located on the coast of Zhejiang Province in southeastern China. There are floating raft culture of ovsters, enclosure seine culture of crab, and cage culture of fish in Xiangshan Bay. We investigated the changes in the number of floating rafts of the oyster culture using the archive WorldView satellite data [19], and we found it seems that not all image fusion methods achieve the same ideal fusion effect [20].