# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 4 Experimental Results and Evaluation
### Results of Comparison Experiments  
Figure 5 presents the CEM loss rate curve for training. As can be seen from the figure, as the number of iterations increases, the loss value decreases and it eventually tends to stabilize. The loss value decreases rapidly during the first 5000 iterations. From iteration 5000 to 15,000, the loss value fluctuates. After 15,000 iterations, the loss value tends to become stable. The initial learning rate set in the training phase is 0.0001, and the learning rate is reduced to half the existing value every 5000 iterations. It can be seen that as the learning rate decreases, the training loss value also slightly decreases within a certain range. This proves that the learning rate gradient has a beneficial effect on the training network. In the experiment of this study, it took 37 h to complete the training and 0.02 s to test an image. Figure 6 presents the extraction results of our model and those of two other models used for comparison with ours. Considering that the goal of this study was to extract crop planting areas, we use Figure 7 to show the results in which the other seven land use types are combined. We use code 9 to present the non-winter wheat. In Figures 6 and 7, a total of 10 sub-images and their extraction results are shown. When selecting these 10 images, we ensured that each image contains winter wheat planting areas and that the other seven land use types appear at least once in an image. This is to enable us to visually compare the algorithms with these results. The first five images are dated 20 February 2017 and the other five are dated 16 March 2018. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Number of Samples in**} & \multicolumn{2}{c}{**Number of Samples in**} & **Number of Samples in** \\  & **Training Data Set (Million)** & **Validation Data Set (Million)** & **Test Data Set (Million)** \\ \hline Winter wheat & 710 & 18 & 9 \\ Agricultural buildings & 2 & 0.1 & 0.07 \\ Woodland & 256 & 6 & 3 \\ Developed land & 542 & 13 & 7 \\ Roads & 23 & 0.6 & 0.3 \\ Water bodies & 25 & 0.7 & 0.3 \\ Farmland & 687 & 17 & 9 \\ Bare fields & 602 & 15 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of samples used in the experiment. Figure 6: Comparison of segmentation results for Gaofen 2 imagery: (**a**) original images, (**b**) ground truth, (**c**) SegNet results, (**d**) RefineNet results, and (**e**) CEM results. Figure 7: Comparison of segmentation results for Gaofen 2 imagery: (**a**) original images, (**b**) ground truth, (**c**) SegNet results, (**d**) RefineNet results, and (**e**) CEM results. Overall, our algorithm shows better performance with the 10 images than that of the other algorithms. Our algorithm can extract winter wheat planting areas of different scales and shapes, and the extracted crop areas are complete and continuous under complex image environments (Figures 6 and 7). In particular, it can be seen from Figures 6 and 7 that in the region where a larger amount of winter wheat grows, the shape extracted by the proposed algorithm agrees well with the actual region; in contrast, other algorithms have more errors at the edges (red areas in the three columns c, d, e). Because our algorithm uses a combination of semantic and spectral features, there is almost no speckle and noise in the extraction results for the crop planting area, and the edge area extraction improvement over other algorithms is also notable. However, in the edge regions, some errors have still appeared, implying that it is necessary to introduce more information to further improve the extraction results. Tables 4-6 present confusion matrices for the segmentation results from the three models. Each row of the confusion matrix represents the proportion of the actual category, while each column represents the proportion of the predicted category. As can be seen from Tables 4-6, the proposed CEM model achieved better extraction results.