Abstract：A supervised classification algorithm with feature selection and deep learning for polarimetric synthetic aperture radar (PolSAR) image is proposed in this paper. Firstly, an original feature parameter set is extracted from the polarization SAR image data and the target decomposition. Then the random forest method is used to evaluate the importance of the feature parameter set. After that, the optimal polarization features are obtained according to the feature importance rank.Taking the optimal polarization feature as the input, the multi-layer feature information is learned by the convolutional neural network (CNN), and the PolSAR image is classified by the trained network model. Experiments are carried out using the measured data collected by the U.S. AIRSAR airborne system, and the results are compared with which of the existing classical supervised classification algorithm. The results show that the proposed algorithm can select effective polarization features and finally obtain more accurate classification results.
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Han Ping, Sun Dandan. Classification of Polarimetric SAR Image with Feature Selection and Deep Learning. Journal of Signal Processing, 2019, 35(6): 972-978.
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2019, Vol. 35 
