*3.3. Identification Accuracy of Di*ff*erent Screening Methods*

The number of features, AIC value, and Kappa coefficient of MLC before and after screening are presented in Table 8. The dimensions and AIC values of all feature sets are reduced by varying degrees after applying the AIC method. When the AIC method was used to screen the high-dimensional feature sets (number of features > 40), the feature dimensions and AIC values decreased significantly: the feature dimension decreased by 90.25% on average, and the AIC value decreased by 88.44% on average. However, for the low-dimensional feature sets, the number of features and the AIC values decreased only slightly after employing the AIC method: the feature dimension decreased by only 31.21% on average, and the AIC value decreased by only 5.83% on average. According to the reference data in Table 8, the average Kappa coefficient of each feature set before screening was 0.03 higher than that after screening. Moreover, the set with the highest Kappa coefficient (0.92) was Texture + canopy structure (CS) + Spectral.


**Table 8.** Feature number, AIC value, and Kappa coefficient before and after using AIC method.

Note: Texture, CS, and Spectral represent the texture feature set, canopy structure feature set, and spectral feature set, respectively.

Here, we show the selected maize lodging features from the Texture + CS + Spectral set for further discussion, which includes the red band, the GLCM mean texture, and dissimilarity texture of the average reflectivity of the red, green, and blue bands; the GLCM mean texture of CHM; and the GLDM mean texture of the average reflectivity of the red, green, and blue bands.
