用深度学习提升高光谱矿物分类抗噪能力，实测在10%噪声下仍保持高精度。
HyDeMiC: A Deep Learning-based Mineral Classifier using Hyperspectral Data
- 基于卷积神经网络，融合真实传感器响应函数生成训练数据
- 在1%-10%噪声下均达接近完美的分类性能（MCC=1.00）
- 适合地质勘探、遥感分析等需应对复杂噪声的实际场景
高光谱成像（HSI）凭借矿物独特的光谱特征，已成为矿产勘查的重要遥感工具。然而，传统分类方法如判别分析、逻辑回归和支持向量机常受环境噪声、传感器限制及高维数据计算复杂性影响。本研究提出HyDeMiC（Hyperspectral Deep Learning-based Mineral Classifier），一种针对噪声数据设计的卷积神经网络模型。训练数据源自美国地质调查局（USGS）数据库中115种矿物的实验室测量光谱，通过卷积参考光谱与实际传感器响应函数生成。以红铜矿、孔雀石和黄铜矿三类含铜矿物为案例进行性能验证。模型在多个含噪（1%、2%、5%、10%）合成二维高光谱数据集上评估，采用马修斯相关系数（MCC）作为综合指标。结果表明，HyDeMiC在无噪声和低噪声条件下达到近乎完美的分类准确率（MCC=1.00），并在中等噪声环境下仍保持强鲁棒性，证明其在真实野外条件下应对噪声挑战的潜力。
原文摘要 · Abstract (English)
Hyperspectral imaging (HSI) has emerged as a powerful remote sensing tool for mineral exploration, capitalizing on unique spectral signatures of minerals. However, traditional classification methods such as discriminant analysis, logistic regression, and support vector machines often struggle with environmental noise in data, sensor limitations, and the computational complexity of analyzing high-dimensional HSI data. This study presents HyDeMiC (Hyperspectral Deep Learning-based Mineral Classifier), a convolutional neural network (CNN) model designed for robust mineral classification under noisy data. To train HyDeMiC, laboratory-measured hyperspectral data for 115 minerals spanning various mineral groups were used from the United States Geological Survey (USGS) library. The training dataset was generated by convolving reference mineral spectra with an HSI sensor response function. These datasets contained three copper-bearing minerals, Cuprite, Malachite, and Chalcopyrite, used as case studies for performance demonstration. The trained CNN model was evaluated on several synthetic 2D hyperspectral datasets with noise levels of 1%, 2%, 5%, and 10%. Our noisy data analysis aims to replicate realistic field conditions. The HyDeMiC's performance was assessed using the Matthews Correlation Coefficient (MCC), providing a comprehensive measure across different noise regimes. Results demonstrate that HyDeMiC achieved near-perfect classification accuracy (MCC = 1.00) on clean and low-noise datasets and maintained strong performance under moderate noise conditions. These findings emphasize HyDeMiC's robustness in the presence of moderate noise, highlighting its potential for real-world applications in hyperspectral imaging, where noise is often a significant challenge.
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