录用：2020-04-16，
纸质出版：2020-06-07
移动端阅览
In recent years
Polarization SAR (PolSAR) has been widely used in the filed of crop biomass estimation. However
high dimensional features extracted from PolSAR data will lead to information redundancy which will result in low accuracy and poor transfer ability of the estimation model. Aiming at this problem
we proposed a estimation method of crop biomass based on automatic feature selection method using Genetic Algorithm (GA). Firstly
the backscattering coefficient
the polarization parameters and texture features were extracted from PolSAR data. Then
these features were automatically pre-selected by GA to obtain the optimal feature subset. Finally
based on this subset
a Support Vector Regression machine (SVR) model was applied to estimate crop biomass. The proposed method was validated using the GaoFen-3 (GF-3) QPSΙ (C-band
quad-polarization) SAR data. Based on wheat and rape biomass samples acquired from a synchronous field measurement campaign
the proposed method achieve relative high validation accuracy (over 80%) in both crop types. For further analyzing the improvement of proposed method
validation accuracies of biomass estimation models based on several different feature selection methods were compared. Compared with feature selection based on linear correlation
GA method has increased by 5.77% in wheat biomass estimation and 11.84% in rape biomass estimation. Compared with the method of Recursive Feature Elimination (RFE) selection
the proposed method has improved crops biomass estimation accuracy by 3.90% and 5.21%
respectively.
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