# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Optimal Feature Selection  
#### 3.2.1 Optimal Pixel Feature Selection  
(1) Confirm the suitable texture-extraction window size for the PlanetScope images  
The extraction window size has a large influence on the textural features of different land cover types. It was thus essential to determine the suitable extraction window size for PlanetScope images to estimate rubber plantation information. Eight kinds of textural features, including the HOM, CON, DIS, ENT, ANG, MEAN, VAR, and COR, were calculated at fifteen window sizes from 3 \(\times\) 3 to 31 \(\times\) 31. Then, the classification importance of each textural feature obtained at the fifteen window sizes was assessed using the RF method. Figure 5 shows that the importance of different textural features varied with the window sizes when distinguishing among different land cover types. As the extraction window size increased, except for the MEAN feature, the importance of the PlanetScope textural features increased with small fluctuations and reached maximal values at the 31 \(\times\) 31 window size. For MEAN, the importance displayed a fluctuating trend as the window size increased, and the maximum value was observed for the 9 \(\times\) 9 window size. Therefore, the MEAN features for the 9 \(\times\) 9 window size and the HOM, CON, DIS, ENT, ANG, VAR, and COR features for the 31 \(\times\) 31 window size were selected as the optimal textural features and combined with the spectral features, index features, and PCA1 to derive pixel-based classifications of the PlanetScope images.  
Figure 4: MODIS-NDVI variation curve for rubber plantations in 2019.  
(2) Optimal pixel-based feature selection  
In this paper, the importance of the 50 initial pixel-based feature variables--including eight spectral features, 33 index features, the PCA1 and eight textural features derived at suitable window sizes--was evaluated by the RF method, and importance ranking was performed according to the feature variable weights (Figure 6). Figure 7 shows the relationship between the number of feature variables and the overall classification accuracy.  
Figure 5: Texture feature importance for different window sizes applied to PlanetScope imagery. Note: The vertical axis represents the feature importance value. The abscissa represents the moving window size.  
Figure 6: Feature weights of different features in the pixel-based approach.  
Figure 7: Relationship between the number of feature variables and the overall pixel-based classification accuracy.  
As shown in Figure 6, the weights of the first six features were greater than 1, especially that of dMSR, which was greater than 2; additionally, the weights of the middle 12 features were between 0.5 and 1, and the weights of the last 32 features were less than 0.5. As shown in Figure 7, with the increase in the number of feature variables, the overall classification accuracy of features 1-26 in the first section exhibited an upwards trend with slight fluctuations, reaching a maximum of 98.47% when the number of features was 26. The overall classification accuracy of features 27-50 in the latter segment gradually stabilized. Thus, as the number of features increased, the numbers of redundant features and related features also increased, thus affecting the accuracy of the classifier. We selected the first 26 feature variables with the maximum overall accuracy as the optimal feature subset, and they include dMSR, dNDVI, NIRtI, MEAN, GNDVItI, REDtI, GREENtI, GREENtZ, EVItz, BLUEtz, HOM, BLUEtI, dGNDVI, REDtZ, NIRtZ, PCA1, NDVItZ, dRVI, NDVItI, COR, EVItI, TVItz, DIS, ENT, GNDVI, and dTVI.