# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 1 Introduction  
The availability of timely and accurate crop spatial distribution information for large areas is very important for scientific research and agricultural management [1; 2]. Traditionally, this information has been obtained through large-scale field surveys. Although this method yields high-precision results, it is time-consuming and labor-intensive [3; 4]. Remote sensing technology quickly provides land surface information covering large areas. The pixel-by-pixel classification of remote sensing images is an effective approach to obtain crop spatial distribution information for large areas [5; 6]. The technique for extraction of more effective pixel features from remote sensing images is key for improving the accuracy of pixel-based classification [7; 8; 9]. Statistically significant spectral and textural features have previously aided in remote sensing image classification problems [10; 11; 12]. A remote sensing index is a formulated spectral feature obtainedfrom mathematical operations on spectral bands, reflecting the information in pixels. The purpose of a remote sensing index is to highlight the information of certain land use types in the image. Frequently employed remote sensing indices include vegetation [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23], water [24], building [25], and ecology [26]. Better commonly utilized indices include environmental vegetation [13; 14], ratio vegetation [15], difference vegetation [16; 19], greenness vegetation [17], perpendicular vegetation [18; 23], soil adjusted vegetation [20], and normalized difference vegetation [21; 22]. The operation rules of the formulated spectral feature are relatively simple and reflect simple statistical information. Consequently, researchers have developed a series of methods for automatically extracting spectral features using computers, including supervised classification methods such as the parallelepiped [27], the minimum distance [27], the Markov distance [28], and the maximum likelihood [29], and non-supervised classification methods, such as the k-means [30]. Textural features describe local patterns and rules on their arrangement in an image, that better describe the spatial correlation of adjacent pixels. Techniques for the extraction of textural information include the Gabor [31], the Gray-Level Co-Occurrence Matrix [32], the Markov random field [33], and the Gibbs random field [34] models, and wavelet transforms [35; 36]. The spectral and the textural features are often combined in practical classification because each provides limited image feature information. The use of high-resolution remote sensing imagery significantly improves the precision and spatial resolution of the extraction results. As traditional feature extraction methods have difficulty extracting effective pixel features from high-resolution remote sensing imagery, researchers have begun using machine learning technology, such as neural networks (NN) [37; 38] and support vector machines (SVM) to extract effective pixel features [39; 40]. As both NN and SVM are shallow-learning algorithms, they also have difficulty extracting high-level semantic features. Compared with NN and SVM, the convolutional neural network (CNN) has the ability to extract high-level semantic features. Standard CNNs work in an \"image-label\" manner, and they output the classes of every image. AlexNet [41], GoogLeNet [42], the Visual Geometry Group (VGG) Network [43], and ResNet [44] are typical standard CNNs. All these models exhibit state-of-the-art image classification performance [45; 46]. A \"per-pixel-label\" model, named the Fully Convolutional Network (FCN), was proposed in 2015 [47]. The FCN utilizes convolutional layers to extract features and employs deconvolutional layers to decode the feature map and restore it to the size of the input image. After FCN was developed, researchers designed a series of convolution-based models to perform pixel-by-pixel classification, such as SegNet [48], DeepLab [49], UNet [50], and RefineNet [51].