2.4.3. Texture Feature Set

Texture features can express the pattern, size, and shape of various objects by measuring the adjacency relationship and frequency of gray-tone changes of pixels [33]. Based on texture features, the spatial heterogeneity of remote sensing images can be expressed quantitatively [34]. The calculation approaches of texture features are usually divided into four types: structural, statistical, model-based, and transformation approaches. At present, the statistical approaches are widely used in lodging recognition research, and the high accuracy of lodging monitoring results can be derived. In addition, the statistical approaches have proved to be superior to structural and transformation approaches. Therefore, the di fferent measurement levels in statistical approaches (the gray-level cooccurrence matrices and gray-level di fference matrices) were selected to calculate the texture features of remote sensing images. The gray-level cooccurrence matrices (GLCM) and gray-level di fference matrices

(GLDM) have been broadly used to quantitatively describe texture features that can be applied in image classification and parameter inversion [35].

To fully describe the details of the lodging and nonlodging maize in the image, we analyzed as many di fferent types of texture features as possible based on the GLCMs and GLDMs. In this study, eight types of GLCM texture measures [36] (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) and five types of GLDM texture measures [37] (data range, mean, entropy, variance, and skewness) were extracted to construct a texture feature set to discriminate between lodging and nonlodging areas. Based on the average reflectivity of three bands (the red, green, and blue bands), red-edge, near-infrared bands and CHM, 41 texture measures were obtained. Specifically, we calculated only the mean texture features (GLCM and GLDM) of CHM to reduce data redundancy.

To speed up the processing, the texture features extracted from CHM were not used when determining the optimal texture window size. Fifteen di fferent sizes of sliding windows were calculated based on the texture feature set without the mean texture features of CHM: 3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, 13 × 13, 15 × 15, 17 × 17, 19 × 19, 21 × 21, 31 × 31, 41 × 41, 51 × 51, 61 × 61 and 71 × 71. Based on maximum likelihood classification (MLC), we analyzed the variations in the Kappa coe fficient and the overall accuracy with the di fferent window sizes. The di fference index (DI) value of the texture feature with the highest classification accuracy was calculated. Finally, the filter window corresponding to the minimum DI value was adopted as the optimal texture window size for maize lodging extraction. To analyze the suitability of the selected optimal texture window, the areas of single lodging and nonlodging maize in multispectral images were measured as 1.55 m<sup>2</sup> and 0.17 m2, respectively. In addition, we measured the area of small-scale maize lodging as 2.21 m2.

The formula for DI calculation is as follows:

$$DI = \frac{SD\_1 + SD\_2}{ABS(MN\_1 - MN\_2)}\tag{1}$$

where *SD1* and *SD2* are the standard deviations of the lodging and nonlodging samples, respectively, and *MN1* and *MN2* are the means of the lodging and nonlodging samples, respectively.
