## 2 Related Work  
The cropland segmentation can be divided into unsupervised and supervised methods. In the unsupervised methods, [PERSON] et al. [6] applied the simulated immersion watershed method to merge the division unit of cultivated land. Their method is designed for merge objects which have been well-segmented from the high-resolution image beforehand. In [7; 8], some object-based methods are presented to single-out croplands in high-resolution remote sensing imagery. In order to improve the discontinuity, preserving smoothing of segmented cropland, normal and uniform kernels are used to filter inner fields and boundary areas [7]. [PERSON] et al. [9] proposed an adaptive threshold method to extract cultivated land from temporal vegetation spectral features. [PERSON] et al. [10] designed a set of mathematical methods for parcel-level boundary extraction from regular arranged agricultural areas. The boundaries of cultivated land extracted by these methods are relatively detailed. However, it is usually necessary to design features or parameters artificially in combination with prior knowledge of the size, shape, and texture of cultivated land in multi-source remote sensing images. For large-scale area cropland extraction, accurately classifying objects from them using the unsupervised approaches is a difficult task. The supervised methods provide a promising way to distinguish the cropland objects in complex scenes containing many uncertainties and intricate relations among classes. For this reason, neural network [11], support vector machine [12; 13], and other machine learning methods [14; 15; 16] are used to build a mapping model between the feature space and the segmentation target, which has become a new development trend. [PERSON] and [PERSON] [17] evaluate how a time-weighted dynamic time-warping (TWDTW) method that uses Sentinel-2 time series performs when applied to pixel-based and object-based classifications of various crop types in three different study areas. From the experimental results, its classification accuracy is better than the random forest method. Using medium-resolution remote sensing images can evaluate the scale of agriculture on a large scale, but the boundaries of the extracted cultivated land are relatively rough, which is coarser than high-resolution image extraction. With the rise of deep learning brings new opportunities to natural image applications, and some of the mature algorithms have already been applied in remote sensing [18; 19; 20; 21]. Convolutional neural network is an important branch of deep learning since it does not require artificially designed feature engineering [22; 23] and can learn local patterns and capture promising semantic information [24]. From the perspective of operational efficiency, it is also known to be efficient compared to other deep learning model types [25]. [PERSON] et al. [26] proposed a full convolutional network (FCN) based on convolutional neural network (CNN), and realized the pixel-level classification of images by replacing the last fully connected layer of the network with an up-sampling layer. Since then, many segmentation algorithms have expanded FCN, from the convolution neural network U-Net [27; 28] at the beginning to the deep neural network models such as Pyramid Scene Parsing Network (PSPNet) [29; 30] and Deeplabv3+ [31; 32]. Some researchers have gradually applied it to the field of remote sensing, especially high-resolution remote sensing images [33; 34; 35]. [PERSON] et al. [36] improved a U-Net network, solved the gradient attenuation problem to a certain extent by adding residual units, and improved the segmentation effect of remote sensing images. [PERSON] et al. [37] proposed a multi-scale adaptive feature fusion network to improve the extraction effect of different scale targets in images. [PERSON] et al. [38] used MFNet as the backbone network, combined with a pyramid pooling model to enhance spatial context information, and used a weighted cascaded loss function to enhance the learning process. [PERSON] et al.