Abstract: Timely and accurate information of crop planting structure is of great significance for monitoring agricultural conditions, estimating crop yield, adjusting agricultural structure and formulating food policies. However, currently only little explicit information about spatial crop patterns is known, especially in China where the farmland landscapes are extremely fragmented and heterogeneous. At present, techniques for quantifying crop spatial patterns may be insufficient to map crops in complex planting areas, the plot sizes of which are smaller than the spatial resolution of ready-to-use satellite data. In order to achieve the fine mapping of crops in complex planting areas, this study aimed to explore approaches that simultaneously mapping multiple crops on parcel scales with high spatial resolution images. A 2.9 km×2.6 km complex heterogeneous planting area in the suburb of Wuhan, Hubei Province was selected as the typical study area. Combined with high spatial resolution images, an improved method of fine crop mapping based on geo-parcels was presented. Using the spectral, shape and texture information of images, combined with random forest (RF), artificial neural network (ANN), and K-nearest neighbor (KNN) algorithms, WorldView-2 images were accurately classified through the following steps. First, Worldview-2 images were visually interpreted to obtain the distribution of cropland and non-cropland in this study area, so as to mask out non-cropland information in remote sensing images. Second, the pre-processed WorldView-2 images were segmented by using the land parcel boundary vector data obtained from manual visual interpretation, and 32 feature variables of the image object were extracted, including NDVI, area, GLCM-correlation, etc. Third, the ReliefF-Pearson feature dimension reduction method was adopted to remove redundant features with high correlation and weak classification ability. Then, RF classification was performed with optimal features and field sampling data, and the accuracy of the classification results was evaluated. Subsequently, the accuracy of RF classification was compared with that of ANN and KNN to verify the effectiveness of RF algorithm. Finally, four feature combination sets were constructed based on optimal features, and RF classification accuracy was compared under four feature combinations to evaluate the contribution of shape and texture features to crop classification. The results showed that 1) The 12 feature variables, such as RVI, NDVI, GLCM-correlation and border length, were the optimal features of parcel-level crop classification based on high spatial resolution images, which can fully characterize image features and reduce data redundancy; 2) The RF method had the highest classification accuracy, with an overall accuracy of 79.07%, kappa coefficient of 0.76, and the overall accuracy of KNN and ANN method was above 70%; 3) Compared with the method of only using spectral features, adding shape or texture information could effectively improve the accuracy of crop classification, and the overall accuracy could be improved by 3.86% and 3.05%, respectively; 4) Based on the optimal features and RF classification method, the classification accuracy of rice, cotton, lotus, bare upland field and bare paddy field was over 80%, while that of abandoned cropland, peanut and 'other crops' was only about 60%. This study provides new ideas, methods and technical means for realizing the fine classification of crops by remote sensing in complex planting areas, and can provide references for accurate survey of crop planting information, refined management of rural land use and dynamic monitoring of agricultural industrial structure adjustment. In the future, the image segmentation technology will be further studied to ensure the consistency between segmentation objects and geographical entities as much as possible, and improve the accuracy and automaticity of crop remote sensing classification on parcel scales.