## 4 Analysis and Discussion
### Advantages of the HSCNN Model  
CNNs have achieved significant success in camera image segmentation, which has motivated researchers to apply them to remote sensing images. The HSCNN model proposed here is developed based on a previous work followed by a further in-depth analysis of the fundamental difference between camera images and remote sensing images. Thus, it possesses clear advantages compared with the traditional practice of the straightforward application of camera image segmentation model to remote sensing images. Camera images and remote sensing images essentially differ in information representation. Owing to their advantages in shooting distance and the pixel quality of the camera, camera images are superior in terms of the rich details they contain, such that one object is formed by multiple pixels. Thus, the color of a pixel reflects the information at a certain point on an object but not the spatial relation between the pixels, which is found and expressed only by convolution. The nature of convolution is to represent the spatial correlation between the pixels by constructing a complex fitting function by operating on the pixel value of a pixel block. Particularly because it makes good use of the essential characteristics of camera images, deep convolution is extremely successful in camera image processing. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Index** & **Experiment-1** & **Experiment-2** & **Experiment-3** & **Average** \\ \hline \multirow{3}{*}{SegNet} & Accuracy & 0.784 & 0.763 & 0.748 & 0.765 \\  & Precision & 0.740 & 0.767 & 0.721 & 0.743 \\  & Recall & 0.718 & 0.766 & 0.720 & 0.734 \\  & Kappa & 0.579 & 0.617 & 0.564 & 0.586 \\ \hline \multirow{3}{*}{DeepLab} & Accuracy & 0.838 & 0.875 & 0.847 & 0.853 \\  & Precision & 0.815 & 0.877 & 0.830 & 0.840 \\  & Recall & 0.778 & 0.877 & 0.852 & 0.836 \\  & Kappa & 0.665 & 0.778 & 0.716 & 0.720 \\ \hline \multirow{3}{*}{HSCNN} & Accuracy & 0.898 & 0.925 & 0.912 & 0.912 \\  & Precision & 0.895 & 0.927 & 0.897 & 0.906 \\ \cline{1-1}  & Recall & 0.853 & 0.927 & 0.921 & 0.900 \\ \cline{1-1}  & Kappa & 0.776 & 0.860 & 0.826 & 0.821 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the approaches using SegNet, DeepLab, and HSCNN. In remote sensing images, particularly for crop fields, a pixel generally contains multiple objects. For example, generally in GF-2 images 1 m\({}^{2}\) of ground is covered by a pixel, which contains 600-700 winter wheat plants. A pixel embodies the color information of the plants and the spatial information between them. However, at the edge of a winter wheat field, the region covered by a pixel is often a mixture of the winter wheat and bare land or winter wheat and other geographical objects, with varying percentages of winter wheat in the space. In this view, the information contained in a pixel at the edge region is significantly different from that at the interior. These two types of pixels can even be regarded as two different types of objects. Based on the above analyses, the HSCNN network architecture is designed with a complete consideration of the properties of the remote sensing image and extraction target, and it makes good use of the characteristics of the winter wheat field captured in the GF-2 remote sensing images. The strengths of this model are exhibited in the following three aspects:  
1. Considering the significant difference between the pixels of the interior and edge region of the winter wheat plantation (during extraction), these two regions are treated as two subclasses. Accordingly, the features of the inner pixels are more focused, which facilitates the model training. Two sub-networks with different depths are then designed with respect to the characteristics of the two subclasses. The deep sub-network extracts the pixels at the interior, whereas the shallower sub-network extracts those at the edge. This scheme reduces the effect of the non-winter wheat pixels on the features and improves the stability of the model for edge pixel extraction. 2.