Accurate and reliable horticultural crop classification results are an important part of agricultural management. At present, research on orchard classification based on optical images in complex mountain areas is vulnerable to the influence of cloudy weather, especially in the summer, which leads to a lack of key phenological characteristics. To solve this problem, a parcel-level orchard mapping experiment with an irregular time series was carried out in Qixia City, China. Firstly, the candidate parcels in the study area were extracted from VHR images with a spatial resolution of 0.55 m based on RCF and DABNet models. The F1 score and area-based intersection-over-union (IoU) of the parcel extraction results were calculated. When the boundary buffer radius was 1 m, the F1 score was 0.93. When the radius was 2 m, the F1 score was 0.96. The IoU was 0.872, which shows the effectiveness of the parcel extraction method. Then, based on Sentinel-2 data, the NDVI, EVI, and SAVI vegetation indexes were calculated to construct an irregular time series. A two-dimensional CNN model was used for classification. In order to verify the effectiveness of this method, the study also constructed regular time series for the study area and conducted classification experiments using the 2DCNN and LSTM as classifiers, respectively. Confusion matrices were constructed for the classification results, and the overall accuracy was calculated. The results show that the overall accuracy of the method based on irregular time series is 97.76%, with a kappa coefficient of 0.96, higher than the other experiments, which indicates that the classification method based on irregular time series is effective and can make full use of the fragmented spectral features. Finally, based on the candidate parcels and pixel-level classification results of the study area, the crop categories of the parcels were filled to achieve accurate parcel-level mapping of horticultural crops in complex mountain areas. This method can provide a theoretical reference for orchard crop mapping and serves the development of regional precision agriculture. © 2022 by the authors.
