Abstract
A meticulous image processing workflow is oftentimes required to derive quality image data from high-resolution, unmanned aerial systems. There are many subjective decisions to be made during image processing, but the effects of those decisions on prediction model accuracy have never been reported. This study introduced a framework for quantifying the effects of image processing methods on model accuracy. A demonstration of this framework was performed using high-resolution hyperspectral imagery (<10 cm pixel size) for predicting maize nitrogen uptake in the early to mid-vegetative developmental stages (V6–V14). Two supervised regression learning estimators (Lasso and partial least squares) were trained to make predictions from hyperspectral imagery. Data for this use case were collected from three experiments over two years (2018–2019) in southern Minnesota, USA (four site-years). The image processing steps that were evaluated include (i) reflectance conversion, (ii) cropping, (iii) spectral clipping, (iv) spectral smoothing, (v) binning, and (vi) segmentation. In total, 648 image processing workflow scenarios were evaluated, and results were analyzed to understand the influence of each image processing step on the cross-validated root mean squared error (RMSE) of the estimators. A sensitivity analysis revealed that the segmentation step was the most influential image processing step on the final estimator error. Across all workflow scenarios, the RMSE of predicted nitrogen uptake ranged from 14.3 to 19.8 kg ha−1 (relative RMSE ranged from 26.5% to 36.5%), a 38.5% increase in error from the lowest to the highest error workflow scenario. The framework introduced demonstrates the sensitivity and extent to which image processing affects prediction accuracy. It allows remote sensing analysts to improve model performance while providing data-driven justification to improve the reproducibility and objectivity of their work, similar to the benefits of hyperparameter tuning in machine learning applications.
Highlights
Remote sensing analysts are expected to ensure that image processing has been performed to a reasonable degree before proceeding with analysis [5,7], but when is it good enough? Even experienced remote sensing data analysts grapple with the following questions: which image processing steps are the most important? Or how should a particular
The objectives of this study were to (i) develop a streamlined software program that performs image processing and carries out the subsequent estimator training and cross-validation in the same workflow, (ii) identify the processing steps that have the greatest influence on prediction error, and (iii) quantify the influence of hundreds of image processing workflow scenarios on prediction error
The distribution of nitrogen uptake represented by the tissue samples within and The 407
Summary
The primary goals of image processing are to improve the signal and to improve the consistency of image data so as to achieve a particular objective or business solution. Different objectives and datasets oftentimes require different image processing workflows [6], and the choice of those workflows is generally subjective in nature. Remote sensing analysts are expected to ensure that image processing has been performed to a reasonable degree before proceeding with analysis [5,7], but when is it good enough? Even experienced remote sensing data analysts grapple with the following questions: which image processing steps are the most important? 2022, 14, 132 processing step (e.g., reflectance conversion) be implemented for my particular use case? There are practical limitations that eliminate the options for how image processing can be performed. Without having access to an integrated downwelling irradiance sensor, real-time changes in illumination cannot be directly measured, and other methods must be employed to quantify illumination changes throughout that image campaign (e.g., by placing multiple reference panels throughout the area of interest)
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