A Novel Approach for Maize Straw Type Recognition Based on UAV Imagery Integrating Height, Shape, and Spectral Information

文献类型： 外文期刊

第一作者： Liu, Xin

作者： Liu, Xin;Gong, Huili;Guo, Lin;Zhou, Jingping;Gong, Huili;Guo, Lin;Gong, Huili;Guo, Lin;Gong, Huili;Guo, Lin;Gong, Huili;Guo, Lin;Gu, Xiaohe;Zhou, Jingping

作者机构：

关键词： maize straw type; multispectral imagery; SESI; object-oriented classification; UAV

期刊名称：DRONES （ 影响因子：4.8； 五年影响因子：5.0 ）

ISSN：

年卷期： 2025 年 9 卷 2 期

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收录情况： SCI

摘要： Accurately determining the distribution and quantity of maize straw types is of great significance for evaluating the effectiveness of conservation tillage, precisely estimating straw resources, and predicting the risk of straw burning. The widespread adoption of conservation tillage technology has greatly increased the diversity and complexity of maize straw coverage in fields after harvest. To improve the precision and effectiveness of remote sensing recognition for maize straw types, a novel method was proposed. This method utilized unmanned aerial vehicle (UAV) multispectral imagery, integrated the Stacking Enhanced Straw Index (SESI) introduced in this study, and combined height, shape, and spectral characteristics to improve recognition accuracy. Using the original five-band multispectral imagery, a new nine-band image of the study area was constructed by integrating the calculated SESI, Canopy Height Model (CHM), Product Near-Infrared Straw Index (PNISI), and Normalized Difference Vegetation Index (NDVI) through band combination. An object-oriented classification method, utilizing a "two-step segmentation with multiple algorithms" strategy, was employed to integrate height, shape, and spectral features, enabling rapid and accurate mapping of maize straw types. The results showed that height information obtained from the CHM and spectral information derived from SESI were essential for accurately classifying maize straw types. Compared to traditional methods that relied solely on spectral information for recognition of maize straw types, the proposed approach achieved a significant improvement in overall classification accuracy, increasing it by 8.95% to reach 95.46%, with a kappa coefficient of 0.94. The remote sensing recognition methods and findings for maize straw types presented in this study can offer valuable information and technical support to agricultural departments, environmental protection agencies, and related enterprises.

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