Abstract
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.
Highlights
With more and more applications of remote sensing images, a variety of remote sensing data can be selected for the same remote sensing data application
6) EVALUATION OF BP NEURAL NETWORK The training sample set is generated from the selected index data, and the generated training samples are input into the Neural Network to train the network
It can be seen that the evaluation result can respond to the index information, indicating that the evaluation model can effectively respond to the image quality
Summary
With more and more applications of remote sensing images, a variety of remote sensing data can be selected for the same remote sensing data application. INDEX TERMS BP neural network, fuzzy set, image quality evaluation model, rough set. In order to show the best image application effect, this paper uses rough set, fuzzy set and neural network theory to establish an image quality evaluation model.
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