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• W2760542535 abstract "Different land-surface parameters reflect the topographic features of the lunar surface from different aspects. Surface roughness is one of the basic land-surface parameters, and the analysis of correlations between surface roughness and other six land-surface parameters, i.e., slope, aspect, curvature, plane curvature, slope variability, and aspect ratio, is very important in scientific research. However, there are no transform functions or related algorithms defined among these parameters. Therefore, this study focuses upon the correlations between the lunar surface roughness and other six land-surface parameters. In the proposed scheme, the morphological roughness method is used to calculate the lunar surface roughness, and other related parameters are processed by their own corresponding formulas. In addition, the Lunar Orbiter Laser Altimeter Gridded Data Records are used as the sample data, and 14 areas on the Moon are selected as the study areas. For each selected area, a backpropagation neural network is presented to find the complex nonlinear correlations between surface roughness and other six land-surface parameters. The experimental results show that there are clear correlations between surface roughness and other six land-surface parameters in each selected area. More specifically, slope, surface curvature, and slope variability have much better correlations with the lunar surface roughness." @default.
• W2760542535 created "2017-10-06" @default.
• W2760542535 creator A5011596136 @default.
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• W2760542535 date "2017-12-01" @default.
• W2760542535 modified "2023-10-17" @default.
• W2760542535 title "Correlation Analysis Between Lunar Surface Roughness and Other Land-Surface Parameters Using BPNN" @default.
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• W2760542535 doi "https://doi.org/10.1109/jstars.2017.2746899" @default.
• W2760542535 hasPublicationYear "2017" @default.
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