# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 3 Results and Discussion
### Comparison of Spectral and Texture Information  
To examine which variable was most influential for classification performance, quantitative measures for variable importance were computed using the MDA in the RF classifier. MDA values of input variables with respect to different kernel sizes of GLCM are shown in Figure 7. Since 54 input variables were used for the classification of six multi-temporal images, only the top nine variables with the highest MDA values are presented for illustration purposes. Regardless of input images and the kernel size of GLCM, NIR and green bands were the most influential variables of the RF classifier. In particular, the NIR bands from July to September were included as important variables for the classification of multi-temporal images. Note that spectral information was more useful than texture information, and only one texture feature, such as ME, was helpful for multi-temporal images. ME, which is an estimate of the intensity of all pixels in spatial relationships that contribute to the GLCM, was the most important variable among the six texture features, irrespective of input images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ClassificationReference** & **Highland Kimchi Cabbage** & **Cabbage** & **Potato** & **Fallow** & **UA (\%)** \\ \hline
**Highland Kimchi cabbage** & 3,074,131 & 342,355 & 49,838 & 84,726 & 86.57 \\
**Cabbage** & 230,661 & 869,250 & 65,288 & 6627 & 74.18 \\
**Potato** & 107,897 & 124,020 & 1,259,483 & 31,883 & 82.68 \\
**Fallow** & 67,045 & 12,343 & 9497 & 375,166 & 80.85 \\
**PA (\%)** & 88.34 & 64.49 & 91.00 & 75.27 & \\
**OA (\%)** & 83.13 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrices and accuracy statistics of some combination cases for the support vector machine (SVM) classifier. VNIR: visible and near infrared; UA: user’s accuracy; PA: producer’s accuracy; OA: overall accuracy; GK31: kernel size of 31 \(\times\) 31. The MDA values of input variables were quite different according to the input images. When six multi-temporal images were used for classification, the MDA value for each variable was relatively small due to contributions of many input variables, but information content provided by many input variables led to very high classification accuracy, as shown in Table 3. Although multi-temporal spectral bands were considered the most informative, the influence of ME increased with kernel size (see the MDA value of ME for GK31 in Figure 7). With classification using only the August image, ME was the second most important variable for GK15 and GK31, indicating that the ME feature is very useful for the classification of crops in the study area. The contribution of other texture features increased with kernel size. For GK3, MDA values of texture features were much smaller than those of spectral bands. With increasing kernel size, gains in MDA values were most significant for texture features, including DIS and ENT. Texture information extracted from the GLCM with the proper kernel size can fill gaps in multi-spectral information, leading to an improvement in classification accuracy, as shown in Figure 6 and Table 3.  
For further qualitative inspection of texture features, some texture features in four subareas of GK31 are provided in Figure 8. Brighter colors represent larger values in each texture feature. ME,  
Figure 8: Some texture features (GK31) in subareas of the August image. Figure 7: Mean decrease accuracy (MDA) values of input spectral and texture variables with respect to kernel size: (**a**) August image; and (**b**) multi-temporal images. ME: mean; ENT: entropy; ASM: angular second moment; STD: standard deviation; HOM: homogeneity; DIS: dissimilarity. which is regarded as the GLCM mean, provides low-pass filtered spatial information that is useful to mitigate noise effects in the ultra-high-resolution UAV image. As an index for measuring the randomness of contrast distributions, ENT increased with greater change of brightness values between the center pixel and its neighboring pixels.