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首页> 外文期刊>Journal of near infrared spectroscopy >The effect of region of interest size on model calibration for soil organic carbon prediction from hyperspectral images of prepared soils
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The effect of region of interest size on model calibration for soil organic carbon prediction from hyperspectral images of prepared soils

机译：感兴趣区域大小对准备好的土壤高光谱图像预测土壤有机碳模型校准的影响

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摘要

Hyperspectral imaging is an attractive technique for soil analysis that provides both spectral and spatial information in a three-dimensional image. Scanning a larger sample area than that permitted in soil spectroscopy allows a larger spatial area to be selected to represent the "average spectra" by means of an interactive region of interest (ROI) tool. The objective of this study was to assess the effect of ROI size on the prediction accuracy of soil organic carbon (SOC) for homogenised soils from a diverse dataset collected on a national scale. Five ROI sizes, 72×72pixels, 54×54pixels, 36×36pixels, 18×18 pixels and 7×7pixels were selected in the near infrared (NIR) region and partial least square calibrations were developed for each ROI size and compared. Cross-validation results demonstrated that increasing the area of sample considered for partial least square regression modelling improved SOC accuracy. Increasing the dimensions of a ROI size by 100-fold reduced root mean square error of cross-validation from 4.60% to 3.88% SOC, a 16% improvement, while R~2 increased from 0.62 to 0.75. Independently validated models showed further improved accuracy whereby root mean square error of prediction was reduced to 3.49% SOC overall, comparable to that reported elsewhere for geographically diverse samples. The spectral variables contributing to the SOC prediction (P<0.05) compared for each ROI size showed that the 7×7 pixels ROI could not differentiate between important and unimportant variables indicating loss of information at this spatial scale.
机译：高光谱成像是一种有吸引力的土壤分析技术，可在三维图像中提供光谱和空间信息。扫描比土壤光谱法允许的更大的样本区域，可以借助交互式关注区域（ROI）工具选择更大的空间区域来表示“平均光谱”。这项研究的目的是评估ROI大小对来自全国范围内收集的各种数据集的均质化土壤的土壤有机碳（SOC）预测准确性的影响。在近红外（NIR）区域中选择了五个ROI尺寸，分别为72×72像素，54×54像素，36×36像素，18×18像素和7×7像素，并针对每种ROI尺寸开发了偏最小二乘标定并进行了比较。交叉验证结果表明，增加用于偏最小二乘回归模型的样本面积，可以提高SOC准确性。将ROI大小的尺寸增加100倍，将交叉验证的均方根误差从4.60％降低到3.88％SOC，提高16％，而R〜2从0.62增长到0.75。独立验证的模型显示出进一步提高的准确性，从而将预测的均方根误差整体降低至3.49％SOC，与其他地区针对不同地理位置的样本所报告的结果相当。对于每种ROI大小而言，有助于SOC预测的光谱变量（P <0.05）表明，7×7像素ROI无法区分重要变量和不重要变量，从而表明在此空间范围内信息丢失。

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