Assessing the quality of UAV-HSIs (Unmanned aerial vehicle hyperspectral images) is crucial for evaluating sensor performance, identifying distortion types, and measuring data inversion accuracy. Due to the absence of reference images, UAV-HSI quality assessment leans towards no-reference image quality assessment (NR-IQA), offering versatile applications. NR-IQA methods of remote sensing images using machine learning techniques have emerged, however, NR-IQA methods for UAV-HSIs containing multi-type and multiple distortions have not been developed. This paper introduces an NR-IQA method for UAV-HSI, employing machine learning techniques. We summarize and simulate distortion types in UAV-HSIs, constructing a quality assessment dataset based on 23 original high-quality and 806 simulated degraded UAV-HSIs. Extracting 129 features encompassing texture, color, transform domain, structural, and statistical aspects, we form seven feature sets through random and filtered feature selection algorithms. Ten machine learning quality assessment models are trained using this dataset and feature sets. The results showed that the model with the highest evaluation accuracy was extra trees (ET) (R2 = 0.928, RMSE = 0.326, RPD = 3.601), using feature set 1 that fuses Tamura texture, color, wavelet transform, and mean subtracted contrast normalized (MSCN) coefficient for a total of 11 features, the PLCC and SROCC of its predicted and true quality scores reached 0.963 and 0.925, respectively. In addition, the random forest (RF), gradient boosting decision tree (GBDT), generalized regression neural network (GRNN), and extreme learning machine (ELM) also had high evaluation accuracies (R2 > 0.9 and RPD > 2.5). These findings underscore the applicability of our proposed machine learning-based NR-IQA method to assess the quality of the UAV-HSIs containing noise, blur, strip noise, and multiple distortions. Additionally, this study serves as a reference for selecting features and models for other hyperspectral image quality assessments.

中文翻译：

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基于畸变构建、特征筛选和机器学习的无人机高光谱图像NR-IQA


评估 UAV-HSI（无人机高光谱图像）的质量对于评估传感器性能、识别失真类型和测量数据反演精度至关重要。由于缺乏参考图像，UAV-HSI 质量评估倾向于无参考图像质量评估（NR-IQA），提供了多种应用。利用机器学习技术进行遥感图像的NR-IQA方法已经出现，然而，针对包含多类型和多种畸变的UAV-HSI的NR-IQA方法尚未开发出来。本文介绍了一种采用机器学习技术的 UAV-HSI NR-IQA 方法。我们总结并模拟了 UAV-HSI 中的畸变类型，基于 23 个原始高质量 UAV-HSI 和 806 个模拟退化 UAV-HSI 构建了质量评估数据集。提取涵盖纹理、颜色、变换域、结构和统计方面的 129 个特征，我们通过随机和过滤特征选择算法形成七个特征集。使用此数据集和特征集训练十个机器学习质量评估模型。结果表明，评估精度最高的模型是额外树（ET）（R2 = 0.928，RMSE = 0.326，RPD = 3.601），使用融合了 Tamura 纹理、颜色、小波变换和均值减去对比度归一化的特征集 1总共11个特征的(MSCN)系数，其预测质量得分和真实质量得分的PLCC和SROCC分别达到0.963和0.925。此外，随机森林（RF）、梯度提升决策树（GBDT）、广义回归神经网络（GRNN）和极限学习机（ELM）也具有较高的评估精度（R2％3E 0.9和RPD％3E 2.5）。 这些发现强调了我们提出的基于机器学习的 NR-IQA 方法在评估包含噪声、模糊、条带噪声和多重失真的 UAV-HSI 质量方面的适用性。此外，本研究可为其他高光谱图像质量评估选择特征和模型提供参考。