## 4 Analysis and Discussion
### Comparison with SegNet and Analysis  
SegNet is founded on the FCN model. Its main strength lies in the search and extraction of the rich details of an image by deep convolution, and it is very distinct when extracting target objects with relatively few pixels. If the target objects contain only a few pixels or even one pixel, the deep convolution does not generate more details and may introduce more noise owing to the expanded field of view, affecting the determination of the pixel type.  
In the remote sensing images of GF-2, the edge and interior of the winter wheat plantation are very different in composition, which makes it more difficult for SegNet to locate the common features, because of its structure containing a single convolutional network. In comparison, the HSCNN is equipped with two sub-networks of different depths and is adaptable to the characteristics of the edge and interior. It also uses two different sizes of kernel, which are capable of uncovering the spatial relation between the pixels, and the information embedded in the pixels.  
As shown in Figure 3, the segmentation results of HSCNN and SegNet are nearly identical for the interior of the winter wheat field. SegNet, however, produces prominent errors at the edge of the field, while HSCNN does not.  
Both HSCNN and SegNet use classifiers to generate the probability distribution of the classes, and consider the class with the maximum probability (max) in the distribution as the type to which the pixel belongs. Clearly, a larger difference between the max and background implies a higher separability of the pixels and more reliable results. The probability differences given by the HSCNN and SegNet model for the inner wheat and edge wheat classes are presented in Figures 4 and 5, respectively. It is clear in Figure 4 that HSCNN and SegNet lead to significant probability differences for many pixels in the interior, which demonstrates the high separability of this region and the strength of CNN. In the probability distribution in Figure 5, fewer pixels are noted as having large probability differences in both the HSCNN and SegNet; nevertheless, the number is maintained at a quite high level for the HSCNN, whereas SegNet exhibits a reduced performance.  
accuracy at the boundary than the FCN and SegNet, because it better utilizes the detailed information contained in the image and the large-scale spatial correlation between the pixels. However, in its application to winter wheat identification, the strength of DeepLab is not fully realized, because the details within a pixel block of the winter wheat plantation do not change significantly. Therefore, less information is available to the model, and the spatial correlation within the farmlands and woods is not strong over large regions.  
As mentioned in Section 4.2, the HSCNN completely utilizes the characteristics of the pixels and the spatial relation between them. Therefore, it is well adapted to the data characteristics of the winter wheat plantation. Further, it effectively avoids the deficiencies of DeepLab and ensures the accuracy of segmentation.  
As in Section 4.2, the probability differences between the HSCNN and DeepLab models in the inner wheat and edge wheat class are displayed in Figures 6 and 7, respectively. It is clear in Figure 6 that both the models produce large probability differences for many pixels in the interior. In the probability distribution of Figure 7, a considerable number of pixels still display large probability differences after the HSCNN processing, whereas DeepLab shows a much poorer performance (even lower than SegNet), proving again the notion that the atrous convolution is not suitable for farmlands.