## 2 Data Sources and Methods
### Network Architecture of Our Method
#### 2.2.1 Inner-Layers and Edge-Layers  
The operational characteristics of the pixel block-based convolution for image segmentation are described in Section 1, in addition to the effect of the pixel block location on the convolution results. Based on this analysis, two convolution sub-structures of different depths are setup for the feature extraction of the winter wheat field. The deep convolution sub-network is used to extract the features of the pixels in the interior of the winter wheat plantation, shown as inner-layers (c) in Figure 1.  
The shallower sub-network is used to extract the features of the pixels at the edge of the winter wheat plantation, shown as edge-layers (g) in Figure 1. The benefits of this design are discussed in Section 4 based on the experimental results.  
In our approach, an inner pixel refers to the pixel that only contains winter wheat pixels in the pixel block when convolution operation is carried out with the pixel as the center pixel. An edge pixel refers to the pixel that contains winter wheat pixels and other pixels when computing the feature of the pixel.  
All kernels of the HSCNN take the form, w \(\times\) h \(\times\) c, where w is the width, h is the height, and c is the number of channels of a kernel. Two types of kernels are used in the first convolutional layers of inner-layers (c) and edge-layers (g). For one type w and h are set to 1, and for the other type the values are set to 3. In both cases, c is set to 4 because the data in the four multi-spectral bands of GF-2 are used. Kernels of the form 1 \(\times\) 1 \(\times\) 4 are used to extract the features of the pixels. The generated feature map is used instantaneously as the input of the encoder, and does not participate in the subsequent convolution. Convolution kernels of the form 3 \(\times\) 3 \(\times\) 4 are used to extract the spatial relation between the pixels and generate the spatial semantics by multi-level convolution.  
After the operation of first convolution layer on the original image, we obtain a feature map which has only one channel. Because the input of convolution layer is the feature map calculated by the previous convolution layer, so the w and h values of the kernels used in all other convolutional layers are set to 3, and c is 1 from the second layer. To extract more features from the edge pixels of the crop field, the number of kernels used in each convolutional layer of edge-layers (g) is twice that used in the corresponding layer of inner-layers (c).  
In the HSCNN, each convolution layer has only one activation layer attached, and there is no pool layer. Accordingly, the convolution result of each pixel block can be used directly as the feature of its central pixel, without the need to determine the position of the pixel that the feature corresponds to through deconvolution. As such, the HSCNN does not utilize a deconvolutional layer. This reduces the extent of computation and positioning error of the deconvolution, thereby improving the accuracy of the segmentation.  
Figure 1: Network architecture of the Hybrid Structure Convolutional Neural Network (HSCNN): (a) input; (b) inner-CNN; (c) inner-layers; (d) inner-encoder; (e) inner-classifier; (f) edge-CNN; (g) edge-layers; (h) edge-encoder; (i) edge-classifier; (j) vote function; (k) output.