Evaluation on BJ-2 Image Fusion Algorithms for Satellite Images of Coastal Aquaculture Sea Areas

Coastal aquaculture sea areas is of great significance to monitor these aquaculture sea areas through the means of a remote sensing technology. The images after fusion are provided with both the high-resolution feature of panchromatic images and the spectral characteristics of multi-spectral images, thereby strengthening the image information and facilitating its application. According to the characteristics of the aquaculture sea area in concern, five fusion algorithms, namely, Brovey Transform, Gram-Schmidt Transform, Principal Component Analysis, Pan Sharp, and Nearest Neighbor Diffusion PanSharpening were adopted to fuse the BJ-2 multi-spectral and panchromatic image data of the areas in the shallow sea raft culture, cage culture, and reclamation culture acquired in Zhangzhou coastal aquaculture sea area of the Fujian Province. Then the fusion images are evaluated subjectively and objectively to provide the best fusion scheme for monitoring aquaculture by remote sensing. The results showed that: (1) the five fusion algorithms are able to significantly improve both the spatial resolution and the utilization ratio of the BJ-2 satellite images; (2) the fusion effects of PSH method can provide the most optimum solution in terms of spectral retentivity and detail expression, and are the best in all the three fusion experiments on aquaculture sea information; (3) the acquired bright-color and high-contrast images make the fusion effects of NNDiffuse the best in terms of image contrast and information enhancement; (4) PSH algorithm is appropriate for use when BJ-2 is used for visual interpretation and thematic charting of shallow sea raft culture, cage culture and reclamation culture and other information; on the other hand, it is recommended to use NNDiffuse for automatic classification and identification.