A Model for Resolving Images of Similar Quality Based on Data Application

Different remote sensing image applications require different quality remote sensing images, and selecting the best quality remote sensing image from multiple remote sensing images with similar quality directly determines the application effect of the image. According to the different applications of remote sensing images, the Rough Set Theory, Fuzzy Set Theory and Back Propagation Neural Network theory (BP Neural Network) are used to establish a remote sensing image quality evaluation model. Combining the basic influencing factors of remote sensing image quality and the influencing factors of specific applications as evaluation indicators, the remote sensing image quality evaluation model is imported, and the best quality remote sensing images are selected in specific applications for remote sensing images of special quality. Finally, this paper proves the feasibility of the model in the selection of the best image quality for specific applications of three different experiments.