Small sample data augmentation and abundances inversion of minerals hyperspectral

ZHU Ling; LI Ming; QIN Kai

doi:10.37188/OPE.20233111.1684

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Small sample data augmentation and abundances inversion of minerals hyperspectral

Information Sciences | 更新时间：2023-07-08

- Small sample data augmentation and abundances inversion of minerals hyperspectral
- Optics and Precision Engineering Vol. 31, Issue 11, Pages: 1684-1690(2023)
- 作者机构：
  
  核工业北京地质研究院遥感信息与图像分析技术国家级重点实验室，北京 100029
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233111.1684
  CLC： TP751
- Received：18 August 2022，
  
  Revised：14 October 2022，
  
  Published：10 June 2023
- 稿件说明：
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朱玲,李明,秦凯.小样本矿物高光谱数据增强与丰度反演[J].光学精密工程,2023,31(11):1684-1690.

ZHU Ling,LI Ming,QIN Kai.Small sample data augmentation and abundances inversion of minerals hyperspectral[J].Optics and Precision Engineering,2023,31(11):1684-1690.
朱玲,李明,秦凯.小样本矿物高光谱数据增强与丰度反演[J].光学精密工程,2023,31(11):1684-1690. DOI： 10.37188/OPE.20233111.1684.

ZHU Ling,LI Ming,QIN Kai.Small sample data augmentation and abundances inversion of minerals hyperspectral[J].Optics and Precision Engineering,2023,31(11):1684-1690. DOI： 10.37188/OPE.20233111.1684.

摘要

为了解决基于深度学习的开展矿物高光谱丰度反演研究中标签数据不足的问题，提出一种基于添加填充系数的Hapke混合模型的小样本矿物高光谱数据增强方法，用于生成大量带标签的数据集。在实验室内选择5种常见矿物按照质量比例对矿物粉末进行多元混合，并对混合矿物开展光谱量测。基于线性混合模型、Hapke混合模型、填充系数分别为0.1，0.2和0.3的Hapke混合模型共5种模型，按照对应的质量比例生成模拟的混合矿物光谱，与实验室实测光谱进行比较。最后，基于Monte Carlo法随机生成多元“和为一”的丰度矩阵，利用5种混合模型开展数据增强，分别生成40 000条模拟光谱作为堆栈自编码网络的训练集，反演矿物高光谱数据的丰度信息。研究结果表明：Hapke模型以及添加填充系数后的光谱模拟精度均优于线性混合模型的模拟精度，当Hapke模型的填充系数为0.1和0.2时，光谱角距离误差均值分别为0.053 5和0.053 7，模拟的矿物光谱更接近实测光谱，且优于未添加填充系数时的光谱角距离误差0.074 8。利用填充系数为0.1和0.2的Hapke模型生成的模拟数据作为深度学习训练集，矿物高光谱丰度反演的均方根误差（RMSE）为0.124 8，优于其他4种模型的反演结果。基于添加填充系数后的Hapke混合模型生成的模拟数据更接近真实光谱，可为深度学习的小样本矿物丰度反演研究提供数据支撑。

Abstract

Using deep learning methods to retrieve mineral abundance requires numerous labeled hyperspectral data samples. Thus， a method based on the Hapke mixed model with filling factor is proposed for data augmentation of small mineral samples， to generate a large number of labeled datasets. First， five kinds of common mineral powders were mixed by multiple elements in the laboratory according to the weight mixing ratio， and the spectra of mixed minerals were measured. Subsequently， mixing spectra were simulated considering the corresponding weight proportion of the five mixing models， including the linear mixing model. The simulated spectra of the augmented data using the original Hapke and the Hapke mixing model with filling factors of 0.1， 0.2， and 0.3 were compared with the measured spectra. Finally， based on the sum to one abundance matrix randomly generated by the Monte Carlo method， forty thousand simulated spectra were generated using the five mixing models. The abundance information on real spectral data was obtained by treating the simulated spectra as the training dataset of the stack autoencoder network. The results showed that the simulation results obtained using the original Hapke model and the model with filling factors were better in accuracy than those of the linear mixed model. When the filling factors of the Hapke model were set to 0.1 and 0.2， the mean SAM error was 0.053 5 and 0.053 7， respectively， and the RMSE error of mineral abundance inversion of hyperspectral data was 0.124 8， demonstrating the superiority of the Hapke model with filling factors over the other four methods. The simulated mineral spectrum was closer to the measured spectrum and better than that without any filling factors with a simulation error of 0.074 8， and the spectra associated with the simulated data were closer to the real spectrum， thereby providing support for mineral abundance inversion research based on deep learning.

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references

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