A Comparison of Different Data Fusion Strategies’ Effects on Maize Leaf Area Index Prediction Using Multisource Data from Unmanned Aerial Vehicles (UAVs)

IF 4.4 2区 地球科学 Q1 REMOTE SENSING
Drones Pub Date : 2023-09-26 DOI:10.3390/drones7100605
Junwei Ma, Pengfei Chen, Lijuan Wang
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Abstract

The leaf area index (LAI) is an important indicator for crop growth monitoring. This study aims to analyze the effects of different data fusion strategies on the performance of LAI prediction models, using multisource images from unmanned aerial vehicles (UAVs). For this purpose, maize field experiments were conducted to obtain plants with different growth status. LAI and corresponding multispectral (MS) and RGB images were collected at different maize growth stages. Based on these data, different model design scenarios, including single-source image scenarios, pixel-level multisource data fusion scenarios, and feature-level multisource data fusion scenarios, were created. Then, stepwise multiple linear regression (SMLR) was used to design LAI prediction models. The performance of models were compared and the results showed that (i) combining spectral and texture features to predict LAI performs better than using only spectral or texture information; (ii) compared with using single-source images, using a multisource data fusion strategy can improve the performance of the model to predict LAI; and (iii) among the different multisource data fusion strategies, the feature-level data fusion strategy performed better than the pixel-level fusion strategy in the LAI prediction models. Thus, a feature-level data fusion strategy is recommended for the creation of maize LAI prediction models using multisource UAV images.
不同数据融合策略在无人机多源数据玉米叶面积指数预测中的效果比较
叶面积指数(LAI)是作物生长监测的重要指标。本研究旨在分析不同数据融合策略对LAI预测模型性能的影响,使用来自无人机(uav)的多源图像。为此,进行了玉米田间试验,获得不同生长状态的植株。采集不同玉米生育期LAI及相应的多光谱(MS)和RGB图像。基于这些数据,创建了单源图像场景、像素级多源数据融合场景和特征级多源数据融合场景等不同的模型设计场景。然后,采用逐步多元线性回归(SMLR)设计LAI预测模型。比较了模型的性能,结果表明:(1)结合光谱和纹理特征预测LAI优于仅使用光谱或纹理信息;(ii)与单源图像相比,采用多源数据融合策略可以提高模型预测LAI的性能;(iii)在不同的多源数据融合策略中,特征级数据融合策略在LAI预测模型中的表现优于像元级融合策略。因此,建议采用特征级数据融合策略,利用多源无人机图像建立玉米LAI预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Drones
Drones Engineering-Aerospace Engineering
CiteScore
5.60
自引率
18.80%
发文量
331
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