基于冠层高度模型的遥感影像玉米倒伏范围提取

doi:10.13474/j.cnki.11-2246.2024.0322

摘要/Abstract

摘要： 精准提取玉米倒伏范围是准确进行田间管理、玉米产量损失估计的基础,无人机获取遥感影像机动灵活,是作物倒伏测量的热门手段。本文提出利用无人技术基于冠层高度差的玉米倒伏范围提取方法。首先通过可见光波段差异植被指数提取玉米背景土壤分布;然后提取玉米的高度;最后基于玉米高度,通过SVM和OSTU自动阈值法提取玉米倒伏范围。试验结果表明,利用SVM法3个样本分类精度分别为88.84%、89.52%和90.80%;OSTU自动阈值法分别为94.61%、89.74%和97.20%,稍优于前者。本文基于作物高度为结构特征参数,提取作物倒伏,机理明确且一定程度上消除了无人机成像不稳定的影响。

关键词: 无人机, 遥感影像, 倒伏, 冠层高度模型, SVM, OSTU

Abstract: Accurate extraction of maize lodging area is the basis of accurate field management and estimation of maize yield loss, and the remote sensing image acquired by UAV is flexible, which is a popular method for crop lodging measurement.However, most of the existing researches use spectral and texture features, which are easily affected by shooting time, terrain, angle and so on. The method of extracting maize lodging range based on canopy height difference is developed by using unmanned technology. Firstly, the background soil distribution is extracted by the visible light band differential vegetation index. And then the height of maize is extracted. Finally, the maize lodging range is extracted based on SVM and OSTU automatic threshold method. The experimental results show that the classification accuracy of SVM for three samples is 88.84%,89.52% and 90.80%,respectively, and for OSTU automatic threshold method is 94.61%,89.74% and 97.20%,respectively, which is slightly better than the former. In this study, crop lodging is extracted based on crop height as a structural parameter. The mechanism is clear and the effect of UAV imaging instability is eliminated to some extent.

Key words: unmanned aerial vehicle, remote sensing images, lodge, canopy height model, SVM, OSTU

中图分类号:

P237

赵莲, 于亚杰, 梁治华. 基于冠层高度模型的遥感影像玉米倒伏范围提取[J]. 测绘通报, 2024, 0(3): 127-133.

ZHAO Lian, YU Yajie, LIANG Zhihua. Extraction of maize lodging range from remote sensing image based on canopy height model[J]. Bulletin of Surveying and Mapping, 2024, 0(3): 127-133.

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