高光谱成像的煤与矸石分类

煤与矸石分选是煤矿生产的必要工序，现有的人工分选与机械分选，存在效率低，易造成资源浪费以及环境污染等问题。鉴于可见/近红外高光谱成像具有分析速度快、样品无需预处理、无污染等诸多优势，旨在探讨基于可见/近红外高光谱成像对黑色背景下块状煤与矸石准确分类的可行性，并基于特征波长筛选算法简化模型，为构建多光谱煤与矸石分选系统提供理论参考。首先，搭建高光谱成像系统并采集山西西铭矿的85个煤样本与83个矸石样本在400～1 000 nm（Vis/NIR）与1 000～2 500 nm（NIR）两个范围内的高光谱图像，基于图像处理方法去除背景信息，选取Vis/NIR范围内100×100像素和NIR范围内50×50像素区域内的平均光谱作为该样本在对应波段范围的一条光谱，重复10次，最终在两个波段各获得煤与矸石光谱850条和830条。其次，对光谱先后进行Savitzky-Golay卷积平滑和标准正态变量变换，以减少噪音和误差对光谱的影响。基于全波段光谱建立支持向量机（SVM），k近邻法（KNN），偏最小二乘判别分析（PLS-DA）三种模型，每个模型针对预测集的分类准确度均大于0.95，结果表明，基于煤和矸石的光谱信息可将二者区分。随后根据竞争性自适应重加权算法（CARS）和连续投影算法（SPA）选择的特征波长建立简化模型，综合考虑精度与成本等因素，在Vis/NIR范围内基于SPA筛选的3个特征波长所建立的SVM模型效果最好，不仅能有效减少波长数量，还能提高模型的分类效果，对应的灵敏度，特异度，准确度分别为1，0.965 2，0.983 3。基于判别模型与样本的平均光谱还可实现煤和矸石的分类可视化。研究结果对开发基于特征波长的低成本煤和矸石多光谱分选系统，实现煤矸快速、准确的无损检测具有借鉴意义。

Classification Method of Coal and Gangue Based on Hyperspectral Imaging Technology

The separation of coal and gangue is a crucial step in coal mining, but the existing methods such as manual selection and mechanical separation are ineffective and environmentally hazardous. This study aimed to explore the feasibility of the accurate classification of coal and gangue with black background based on the visible and near-infrared hyperspectral imaging technology, simplify classification models using feature selection methods, and provide a reference for constructing a multispectral system for coal and gangue separation. Hyperspectral imaging technology is a fast and non-destructive detection method without sample pretreatment and environmental contamination. Firstly, a hyperspectral imaging system was developed to collect hyperspectral data of 85 coal samples and 83 gangue samples in the range of 400～1 000 nm (Vis/NIR) and 1 000～2 500 nm (NIR) from the XiMing mine. After removing background information of hyperspectral images, the average spectra in the randomly selected regions of 100 pixel×100 pixels in 400～1 000 nm and 50 pixel×50 pixels in 1 000～2 500 nm were extracted. After repeating 10 times, 850 coal spectra and 830 gangue spectra were obtained in each of the two bands. Savitzky-Golay smoothing and standard normal variate transformation were performed successively to reduce the impact of errors and noise on the spectra. Three models, including support vector machine (SVM), k-nearest neighbor (KNN), partial least squares discrimination analysis (PLS-DA), were established based on full-band spectra. The classification accuracy rate of each model for the prediction set was greater than 0.95, which revealed that coal and gangue could be distinguished by spectral information. Subsequently, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were employed to select characteristic wavelengths to simplify models. Considering factors such as accuracy and cost, the SVM model based on the 3 characteristic wavelengths screened by SPA in the Vis/NIR range had the best performance, that not only effectively reduced the number of wavelengths, but also improved the classification capacity and the corresponding sensitivity, specificity, accuracy was: 1.000 0, 0.965 2, 0.983 3, respectively. Based on the discriminant model and the average spectra of the samples, the classification and visualization of coal and gangue can also be realized. The research results have great potential for developing a low-cost and multi-spectral separation system for coal and gangue based on the characteristic wavelengths to achieve fast and accurate non-destructive separation.