Abstract: Farmland parcel data can be accurately and rapidly acquired using remote sensing in modern agriculture. In this study, a prior knowledge and semantic features of integration-based farmland parcel extraction (PKFFPE) was proposed to accurately extract the winter wheat data from the high-resolution remote sensing images. The high-resolution remote sensing images were taken as the data source. Three modules were divided into the PKFFPE: an initial segmentation, a post-processing and an extraction module. Among them, the initial segmentation module was used to construct and then train a convolutional neural network model for the pixel-by-pixel classification. The post-processing module was to optimize the initial segmentation. The extraction module was to accurately extract the farmland parcel data from the inputted remote sensing images. The initial segmentation module of PKFFPE consisted of three parts: input, feature extractor and classifier. The input included the remote sensing and edge image blocks. It was also necessary to add the pixel-by-pixel labels into the input in the training stage; The feature extractor was employed an encoder-decoder structure. Two units of feature extraction were utilized to extract the semantic and edge features. The semantic feature extraction employed a multi-scale attention mechanism, including an improved channel and spatial attention mechanism. A multi-scale feature extraction was used to capture the features at different scales. The decoder shared the six levels of decoding units, each of which contained a number of convolutional layers. The columns and rows of the feature map were recovered, according to the inputted remote sensing image block each time. A SoftMax model was used as the classifier to classify each pixel, according to the feature map output from the decoder. Each pixel was labeled as either a winter wheat or a non-winter wheat. According to the prior knowledge derived from planting management, the pixels within the same farmland parcel generally exhibited the high consistency in the basic features, such as color and texture. While there were typically greater differences in these features, compared with the adjacent objects, such as the roads. The prior knowledge was obtained from the distribution patterns of color and texture in the same farmland parcel. The optimization was performed on the initial segmentation after post-processing, in order to generate the accurate farmland parcel data. The applicability of the PKFFPE was verified in the plain and hilly areas. The study areas were taken from Guantao County in Handan City, Hebei Province, and Ningyang County in Tai'an City, Shandong Province, China. The UNet, ErfNet, SegNet, EIGNet, and object-oriented classification models were selected to compare the initial segmentation. While conditional random fields and mathematical morphology were selected to evaluate the post-processing. The experimental results show that the accuracy (96.1%, 92.3%), precision (90.6%, 87.6%), recall (93.2%, 90.6%), and F1 coefficient (91.9%, 89.0%) of the PKFFPE in Guantao and Ningyang counties were better than the rest. The PKFFPE performed the best to accurately extract the farmland parcel data from the high-resolution remote sensing images. The findings can also be applied to scientific research and practical production