Scattering entropy ( H ), scattering angle ( α ) and anti‐entropy ( A ) are useful parameters in synthetic aperture radar (SAR) image classification. Usually, full‐polarimetric SAR data are needed to extract these parameters. In this study, the authors firstly try to predict these parameters from single/dual‐polarimetric SAR data using convolutional neural network. Experiments are done on GF‐3 polarised SAR database, and promising results are obtained, where the parameters H and α , the average relative error reached is <10%, the parameter A , the average relative error reached is around 25%, and the classification performance based on predictive parameters is around 80%. Furthermore, the predicting performance using different single‐ and dual‐polarisation is compared. The results and conclusions provide a new clue for the applications of single/dual‐polarimetric SAR.