Abstract
Uncertainty is the most important factor affecting the quality of the remote sensing image classification. Aiming at the characteristics of both the random and the fuzzy uncertainties in the process of the remote sensing image classification, a method based on the mixed entropy model is proposed to measure these two uncertainties comprehensively, and a multi-scale evaluation index is established. Based on the analysis of the basic principles of the mixed entropy model, a method of using the statistical data of the feature space and the fuzzy classifier to establish the information entropy, the fuzzy entropy and the mixed entropy is proposed. At the same time, on the scale of the pixel and the category, the index of the mixed entropy of the pixel and the mixed entropy of the category are established to evaluate the uncertainty of the classification.
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Zhang, Y. (2020). Research on the Agricultural Remote Sensing Image Enhancement Technology Based on the Mixed Entropy Model. In: Xu, Z., Choo, KK., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-15235-2_22
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DOI: https://doi.org/10.1007/978-3-030-15235-2_22
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