A Novel Feature Construction Method for Tobacco Chlorophyll Estimation Based on Integral of UAV-Borne Hyperspectral Reflectance Curve

文献类型： 外文期刊

第一作者： Zhang, Mingzheng

作者： Zhang, Mingzheng;Chen, Tian'En;Gu, Xiaohe;Jiang, Shuwen;Chen, Dong;Zhao, Chunjiang;Zhang, Mingzheng;Chen, Tian'En;Gu, Xiaohe;Jiang, Shuwen;Chen, Dong;Zhao, Chunjiang;Zhang, Mingzheng;Zhang, Jiuquan;Kuai, Yan;Zhu, Qingzhen

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关键词： Feature extraction; Data models; Reflectivity; Crops; Soil; Nitrogen; Hyperspectral imaging; Chlorophyll; feature construction; hyperspectral; integral; segmented fitting; tobacco

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.6； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2024 年 62 卷

页码：

收录情况： SCI

摘要： Chlorophyll, a key pigment in leaf photosynthesis, is crucial for monitoring tobacco growth, evaluating quality, and determining optimal harvest timing. Hyperspectral remote sensing (HRS) via unmanned aerial vehicles (UAVs) provides a viable method to assess tobacco leaf chlorophyll content (LCC) due to its real-time and high-throughput capabilities. However, the existing spectral feature extraction methods often suffer from variability due to external factors or internal parameters, resulting in unstable results. To address this, we proposed a novel feature construction method, termed segmented fitting for integral (SFI). This method divides the entire spectral curve into five areas based on the spectral response properties of chlorophyll and nitrogen and then selects a suitable fitting function for each area to calculate the integrals. This idea not only fully utilizes the spectral reflectance and curve shape characteristics but also remains largely unaffected by external factors and internal parameters. To further verify the stability and predictive capability of the SFI method, we compared it against three different feature extraction methods, including the successful projections algorithm (SPA), the recursive feature elimination (RFE), and the principal component analysis (PCA), and also with two ensemble learning-based modeling approaches, namely, random forest (RF) and adaptive boosting (AdaBoost). The results demonstrated that the SFI method can effectively reduce data dimensionality and enhance model performance. Finally, we applied the best-performing SFI-AdaBoost model in field prediction to generate chlorophyll distribution and error maps, which closely aligned with actual chlorophyll measurements and demonstrated its potential for practical evaluation of tobacco LCC.

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