## 1 Introduction  
Winter wheat is the most important food crop in China, comprising 21.38% of the gross cropped area of the domestic food crops in 2017 according to the data released by the National Bureau of Statistics, with its output accounting for 21.00% of the total food crop production [1]. For national food security, the Chinese government has assigned a minimum area of arable land in each region that needs to be safeguarded (the \"red line\") [2]. Timely and accurate acquisition of the size and spatial distribution of winter wheat fields assists the relevant government departments in guiding the farming activities, estimating the yield, and adjusting the agricultural structure for ensuring food security [3]. Remote sensing is capable of imaging and large-area monitoring, making it a good data source for rapid and accurate extraction of winter wheat planting information. Researchers have successfully extracted winter wheat spatial distribution information from MODIS (moderate-resolution imaging spectroradiometer) and ETM/TM (enhanced thematic mapper plus/thematic mapper), achieving accuracies of 85.5% and 89.1%, respectively [4; 5]. This exhibits the advantage of remote sensing in this application. However, owing to limitations in the spatial resolution of the data source, the spatial resolution of the extraction results is also rather coarse and unable to satisfy the requirement of the application [6; 7; 8; 9; 10]. With the development of high-resolution remote sensing satellites, a crop planting area can be monitored more accurately using the corresponding images as the data source [11; 12]. The winter wheat cultivation information is extracted from the remote-sensing images captured by Gaofen-1 of the Gaofen series of Chinese satellites, yielding satisfactory results, with maximum accuracy reaching about 89% [13; 14; 15; 16; 17; 18]. Most researchers still use traditional methods, such as decision trees and textures features. These methods can only take advantage of low-level features, which make it easy to make mistakes in identifying pixels at the edge of winter wheat planting area. Image segmentation has been successfully used in the processing of camera images and applied by researchers to high-resolution remote sensing images, achieving significantly more accurate classification by a pixel-by-pixel segmentation [19; 20; 21]. Feature extraction is the key step in remote sensing image segmentation. In high-resolution remote sensing images, as the spectral difference between the same type of objects is increased, and between different types of objects is diminished, the former has more probability of exhibiting different spectral properties, whereas the latter tends to be spectrally similar, which makes feature extraction increasingly difficult [22; 23]. Traditional methods including k-nearest neighbors and maximum entropy can only identify low-level image features such as color, shape, and texture. They are not capable of visually providing a semantic description. This hinders the extraction of higher-level features and limits the use of these methods in the segmentation of high-resolution remote sensing images [24; 25]. With the development of machine learning, algorithms such as neural networks (NNs) [26] and support vector machine (SVM) [27; 28] are being used in the segmentation of high-resolution images [29; 30; 31]. In some studies, when compared with traditional statistical methods and object-oriented methods, machine learning algorithms yielded better image segmentation results [32; 33]. Both SVM and NNs are shallow-learning algorithms [34; 35; 36], which do not express complex functions well owing to the limitations in their network structure. Therefore, these models cannot adapt to the continuously increasing complexity caused by the increasing sample size and diversity [37; 38]. Progress in deep learning has facilitated solving these problems by using deep neural networks (DNNs) [39; 40; 41; 42].