用于紧密混合物解混的多传感器高光谱基准数据集
A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures
AI总结：
本文构建了一个包含325个紧密混合矿物粉末样本的多传感器高光谱基准数据集，覆盖350-15385 nm波段，并经XRD和XRF验证，旨在推动非线性解混、材料成分估计及光谱变异性研究。
摘要：
光学高光谱相机捕获材料的光谱反射率。由于许多材料表现为异质紧密混合物，每个光子与其相互作用不同，光谱反射率与材料成分之间的关系非常复杂。光谱解混算法的定量验证需要高质量的地面真实丰度数据，而这非常难以获得。在这项工作中，我们生成了一个由紧密混合矿物粉末组成的综合实验室地面真实数据集。为此，将五种粘土粉末（Kaolin、Roof clay、Red clay、mixed clay 和 Calcium hydroxide）均匀混合，制备了325个样本，包括60个二元、150个三元、100个四元和15个五元混合物。使用十三种不同的高光谱传感器在可见光、近红外、短波、中波和长波红外区域（350-15385）nm获取了这些混合物的反射光谱。由于每个传感器的工作范围导致的波长区域重叠，以及采集条件的变化，产生了大量的光谱变异性。地面真实成分由构建过程给出，但为了验证生成的样本足够均匀，进行了XRD和XRF元素分析。我们相信这些数据将有利于验证非线性解混和材料成分估计的先进方法，包括研究光谱变异性和训练监督解混方法。数据集可从以下链接下载：https://github.com/VisionlabUA/Multisensor_datasets。
英文摘要：
Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain. In this work, we generated a comprehensive laboratory ground truth dataset of intimately mixed mineral powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60 binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen different hyperspectral sensors have been used to acquire the reflectance spectra of these mixtures in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm. {\color{black} Overlaps in wavelength regions due to the operational ranges of each sensor} and variations in acquisition conditions {\color{black} resulted in} a large amount of spectral variability. Ground truth composition is given by construction, but to verify that the generated samples are sufficiently homogeneous, XRD and XRF elemental analysis is performed. We believe these data will be beneficial for validating advanced methods for nonlinear unmixing and material composition estimation, including studying spectral variability and training supervised unmixing approaches. The datasets can be downloaded from the following link: https://github.com/VisionlabUA/Multisensor_datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
