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

文献类型： 外文期刊

作者： Sun, Wei 1 ; Zhang, Rong 1 ; Wei, Shuhua 1 ; Liu, Jianping 2 ; Wang, Jian 3 ;

作者机构： 1.Ningxia Acad Agr & Forestry Sci, Inst Plant Protect, Yinchuan 750002, Peoples R China

2.North Minzu Univ, Coll Comp Sci & Engn, Yinchuan 750021, Peoples R China

3.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

关键词： Agriculture; Convolutional neural networks; Remote sensing; Feature extraction; Satellites; Radio frequency; Data mining; Crop yield; Time series analysis; Corn planting plots extraction; improved HRNet; convolutional block attention module (CBAM); Sentinel-2; NDVI time series

期刊名称：IEEE ACCESS （ 影响因子：3.6； 五年影响因子：3.9 ）

ISSN： 2169-3536

年卷期： 2024 年 12 卷

页码：

收录情况： SCI

摘要： 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.

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