Method for Extracting Corn Planting Plots in the Loess Plateau Region Based on the Improved HRNet and NDVI Time Series

Abstract

Corn is a major cereal crop, and accurate monitoring of corn planting areas is crucial for agricultural structural adjustments and ensuring food security. This study proposes an improved HRNet network that utilizes the spectral and spatial features of Sentinel-2 to extract synthetic NDVI time series datasets for identifying corn planting plots. The study involves enhancing the HRNet network by integrating the CBAM attention mechanism and FReLU activation function, processing the 2023 corn planting growth period data in the Loess Plateau region of Pengyang County, Ningxia, China. This is achieved through: 1) preprocessing Sentinel-2A data and constructing smoothed time series data, and 2) conducting field data surveys to create training, validation, and testing sets. Subsequently, the improved HRNet network is utilized to extract corn planting plots in the study area, followed by accuracy assessment. The results demonstrate that the proposed method achieves accuracy (Acc), F1 score, and mean Intersection over Union (mIoU) of 91.06%, 90.82%, and 88.58% respectively, outperforming PSPNet, U-Net, and HRNet networks. Furthermore, it is proven that using the NDVI time series dataset for all months can enhance identification accuracy. This research confirms that the proposed method has high potential and applicability in identifying corn planting areas in the Loess Plateau region.

Keywords

• Corn planting plots extraction
• improved HRNet
• convolutional block attention module (CBAM)
• Sentinel-2
• NDVI time series