Hyperspectral Inversion of Soil Cu Content Based on CWT-sCARS
The practical degree of spectral variables is closely related to the inversion accuracy of soil cop-per content.Based on the original reflectance and the wavelet coefficients at different decomposition scales,the continuous wavelet transform ( CWT) algorithm,the stability-competitive adaptive reweighted sampling ( sCARS) algorithm,and the random forest ( RF) algorithm were used in this study to invert and validate the soil copper content.The results showed that the continuous wavelet transform can effectively improve the correlation between spectral features and soil copper content.Among the maximum correlation coefficients corresponding to different decomposition scales,the maximum value is located at 1343 nm un-der the Scale 8 decomposition scale,with a correlation coefficient 0.60.The use of the sCARS algorithm significantly reduces the number of feature variables.Combining the CWT transform and the sCARS algo-rithm can significantly reduce the data redundancy and improve the inversion accuracy of the soil Cu con-tent .It can provide an essential reference in this study for the rapid and high-precision inversion of soil Cu content using hyperspectral remote sensing technology.
hyperspectral inversioncontinuous wavelet transformstability of competitive adaptive reweighted sampling
万方数据
维普
