## 1 Introduction
As an important branch of deep learning, a convolutional NN (CNN) is widely used with visual data because of its excellent feature learning ability [43; 44; 45]. A CNN is a deep learning network, composed of several layers, capable of nonlinear mapping. Its strength in learning is exemplified by the good image segmentation results achieved [46; 47; 48; 49; 50; 51; 52]. Further, the capacity of many large CNNs can be scaled according to the size of the training data and complexity and processing ability of the model, and their performance in image segmentation has improved significantly [53; 54; 55; 56; 57; 58; 59; 60]. A fully convolutional network (FCN) is a deep learning network for image segmentation, which was proposed in 2015. Taking advantage of convolution computation in its feature organization and extraction abilities, an FCN realizes pixel-by-pixel segmentation of camera imagesby constructing a multi-layer convolutional structure and setting appropriate deconvolutional layers [61; 62; 63]. Accordingly, a series of convolution-based segmentation models has been developed including SegNet [64], UNet [65], DeepLab [66], multi-scale FCN [67], and ReSeg [68]. Of these models, SegNet and UNet are clearly structured, and it is easy to understand the convolution structure of the model. The processing speed is fast. DeepLab uses a method called \"Atrous Convolution\", which has a strong advantage in processing detailed images. multi-scale FCN is designed to address the huge scale gap between different classes of targets, i.e., sea/land and ships. ReSeg exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. Each model has its own strengths and is adept at dealing with certain image types. In the work of extracting the spatial distribution of crops with high GF-1 as the data source, in addition to methods such as decision trees, textures features, and maximum entropy, research has also been carried out using deep learning. However, most of these studies directly use the existing deep learning model as a tool, and seldom consider the influence of characteristics difference of edge pixels and inner pixels in the crop planting area are large. On board the Gaofen-2 satellite is a panchromatic camera with a spatial resolution of 1 m, and a multi-spectral camera with a spatial resolution of 4 m, which provides ideal data for extracting winter wheat plantation information. Before the application of a CNN to GF-2 remote-sensing images for this purpose, trial extraction is performed with classical network architectures (such as SegNet) where misidentified pixels are categorized, of which approximately 90% are found at the edge of the crop field. Further analysis indicates the structure of the convolutional layer as the source of this problem. The outcome produced by operating the convolution kernel in the pixel block is treated as the eigenvalue of the central pixel of the pixel block. As such, for the pixels at the edge, 50% of the pixels involved in each convolution are from negative samples, whereas, for the pixels at the corner, this number is 75% or higher. This results in a significant difference between the eigenvalues of the pixels at these locations and those at the center of the image, and an increase in the probability of the recognition results being placed in a wrong category. To avoid these problems, a new method is herein proposed for the extraction of the winter wheat field information from the GF-2 remote sensing images. The main procedures are as follows. 1. First, a CNN consisting of two independent sub-networks of different depths is established. The deep and shallow sub-networks are trained to be sensitive only to the pixels at the interior and edge of a winter wheat planting field, respectively, and only these pixels are extracted. This model is named as a Hybrid Structure Convolutional Neural Network (HSCNN). 2. A classification algorithm is adopted in the model training.