Crop Classification in Mountainous Areas Using Object-Oriented Methods and Multi-Source Data: A Case Study of Xishui County, China

Abstract

Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method to address this issue, using multi-source data and object features to achieve multi-crop classification in mountainous areas. Firstly, a deep learning method was employed to extract cropland field parcels in mountainous areas. Subsequently, the fusion of multi-source data was carried out based on cropland field parcels, while object features tailored for mountainous crops were designed for crop classification. Comparative analysis indicates that the proposed classification method demonstrates exceptional performance, enabling accurate mapping of various crops in mountainous regions. The F1 score and overall accuracy (OA) of the proposed method are 0.8449 and 0.8502, representing a 10% improvement over the pixel-based random forest classification results. Furthermore, qualitative analysis reveals that the proposed method exhibits higher classification accuracy for smaller plots and more precise delineation of crop boundaries. Finally, meticulous crop mapping of corn, sorghum, rice, and other crops in Xishui County, Guizhou Province, demonstrates the significant potential of the proposed method in crop classification within mountainous scenarios.