# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 5 Discussion
### SegNet versus CEM  
The SegNet model extracts high-level semantic features from rich image details through deep convolution. It demonstrates advantages when extracting objects that cover a large number of pixels. However, if the number of pixels covered by the object is small, or a pixel contains several target objects, this structure not only fails to extract detailed features, but may introduce additional noise due to the expansion of the field of view. This reduces the accuracy of the classification results from the model. When extracting crop spatial distributions from a GF-2 image, although the number of pixels occupied by a farmland is large, the difference between pixels remains small because the area covered by a plant is small, and this obscures the advantages of SegNet.  
Contrary to the SegNet model, which extracts 64 high-level semantic features by deepening the hierarchy, the CEM model extracts 80 features by combining spectral features, high-level semantic features, and low-level semantic features. Because CEM fully considers the natural features of crops and the distribution characteristics of farmlands, it is advantageous for identifying pixels at the edges and corners of crop planting areas.  
In summary, there are the following differences between CEM and SegNet model.  
(1) The SegNet model transforms the highest-level semantic features into pixel feature vectors by step-by-step sampling. Therefore, pixel feature vectors only contain abstract semantic information. The CEM model adopts feature fusion to fuse low-level semantic feature information and high-level semantic information, so the information contained in feature vectors of pixels is more abundant than that of SegNet.  
(2) The feature vector generated by the SegNet model has only semantic information. The feature vector generated by the CEM model not only has semantic information, but also statistical information of the spectral values of the pixels themselves.  
(3) Although the pooling method adopted by SegNet has the effect of aggregating feature values, but each pooling reduces the size of feature map to 1/4 of the original size, which is not conducive to the generation of pixel feature vectors. The pooling method adopted by CEM not only achieves the purpose of aggregation of feature values, but also reduces the degree of feature map size reduction, which can help to obtain feature vectors with good discrimination.  
Figure 9 shows a comparison of the results of the two models. The images in Figure 9 are dated 20 February 2017. As can be seen from Figure 9, only a few pixels are misclassified at the corners in case of the CEM results, while a significantly higher number of pixels are misclassified in the SegNet results, both at the corners and edges, and even within the planting area. In Regions 1 and 2, SegNet's results show more severe jaggedness, and our model has almost no jaggedness. In Regions 3, 4, 5, and 6, SegNet does not make a recognition at all, and our model's recognition effect is very consistent with  
Figure 8: The category confidence distribution for the results of the experiment.  
the actual regions, indicating that our model overcomes the defects of the SegNet model. Further, the use of only high-level semantic features is an important cause of edge roughness. The use of low-level semantic features and high-level features through fusion is an important way to improve the edge.