## 2 Materials and Methods
### Data Introduction  
In this study, we selected the GF-6 [12] satellite for its representative GF-6 PMS data, which is highly suitable for detailed land cover classification due to its superior spatial and spectral resolution. To enhance data quality and ensure the accuracy of our analysis, we applied the following preprocessing steps to the original remote sensing data: radiometric correction, atmospheric correction, orthorectification, and image fusion. The processed remote sensing images were preserved with four spectral bands: Band 1 (Red), Band 2 (Blue), Band 3 (Green), and Band 4 (Red Edge). We chose imagery from four critical phenological stages of winter wheat: 1 November 2020; 2 December 2020; 4 February 2021; and 6 April 2021.  
ACGF Dataset: The ACGF dataset. Developed by processing GF-6 PMS data, we have constructed the ACGF dataset for the classification of 129 land cover features. This dataset comprises 8386 images showcasing various land cover types in remote sensing imagery. The classification categories were determined based on the prominent land cover types evident in remote sensing imagery: (1) Winter Wheat, the primary agricultural crop; (2) Urban Buildings, encompassing various urban constructions and infrastructure; (3) Water Bodies, including rivers, lakes, etc.; (4) Uncultivated Farmland, denoting areas not effectively developed or utilized; (5) Roads, covering transportation networks; (6) Other Crops, including agricultural crops other than winter wheat, noting the increased segmentation challenge due to the fragmentation of winter wheat fields and the complexity of the surrounding areas. The selection of these categories reflects the diversity of our study and underscores the necessity for accurate land cover classification.  
The LoveDA [13] dataset consists of 0.3 m high-resolution images from Google Earth of three different cities, including land cover types such as buildings, roads, green spaces, bare land, water bodies, cultivated land, and others.  
The LandCoverNet [14] dataset is primarily made up of 10 m resolution images from Sentinel-2, covering a wide range of geographic and ecological environments globally. It includes land cover types such as buildings, forest land, grassland, bare land, shrubland, water bodies, cultivated land, snow/ice, and submerged vegetation.  
The DroneDeploy [15] dataset comprises 10 cm high-resolution aerial images captured by drones, including land cover types such as buildings, debris, vegetation, water bodies, buildings, ground, and vehicles.  
The PASCAL Visual Object Classes (VOC) [16] dataset is a significant dataset in the field of computer vision research, utilized by the Pattern Analysis, Statistical Modeling, and Computational Learning (PASCAL) network in the Visual Object Classes Challenge. This dataset comprises a large number of images captured from various angles, covering a wide range of object categories including people, animals, vehicles, and furniture. The diverse backgrounds of these images increase the difficulty of recognition and segmentation tasks. Each image is provided with detailed annotation information, including object bounding boxes (for object detection tasks) and pixel-level object contours (for image segmentation tasks). The PASCAL VOC has had a profound impact on the advancement of the computer vision field, particularly in object recognition and image segmentation technologies.