Winter Wheat Mapping Method Based on Pseudo-Labels and U-Net Model for Training Sample Shortage

作者：Jianhua Zhang, Shucheng You, Aixia Liu, Lijian Xie, Chenhao Huang, Han Xu, Penghan Li, Yixuan Wu, Jinsong Deng · 发表于：Remote Sensing · 年份：2024 · DOI：10.3390/rs16142553 · 被引用次数：14 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Remote-Sensing Image Classification

In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true label data for major crops in China, and the visual interpretation process is usually time-consuming and laborious. The sample size also makes it difficult to support the model to learn enough ground features, resulting in poor generalisation ability of the model, which in turn makes the model difficult to apply in fine extraction tasks of large-area crops. In this study, a method to establish a pseudo-label sample set based on the random forest algorithm to train a semantic segmentation model (U-Net) was proposed to perform winter wheat extraction. With the help of the GEE platform, Winter Wheat Canopy Index (WCI) indicators were employed in this method to initially extract winter wheat, and training samples (i.e., pseudo labels) were built for the semantic segmentation model through the iterative process of “generating random sample points—random forest model training—winter wheat extraction”; on this basis, the U-net model was trained with multi-time series remote sensing images; finally, the U-Net model was employed to obtain the spatial distribution map of winter wheat in Henan Province in 2022. The results illustrated that: (1) Pseudo-label data were constructed using the random forest model in typical regions, achieving an ove...