基于高光谱激光雷达信号强度免校准的煤岩分类

安全性对于深井开采至关重要。基于激光雷达的扫描探测技术可以有效监测巷道和深部煤矿现场的围岩安全状况。新兴的高光谱激光雷达不仅可以提供空间几何信息还可以提供丰富的光谱数据，在深井煤矿安全检测和精细结构分析方面具有良好的应用前景，而精确的煤岩分类是监测分析的基础。在实际应用中，雷达强度信号易受仪器属性和环境因素的影响，需校准才能使用。由于深井煤矿粉尘污染严重，常规校准方法难以达到理想效果。针对这个问题，提出一种信号强度免校准的方法，从激光雷达回波信号中提取新特征实现煤岩精确分类。首先，使用高光谱激光雷达获得煤/岩石样本的回波强度信息，并计算出波形熵（WE）和联合偏斜度-峰度系数（JSKF）作为新分类特征参数。其次，采用随机森林(RF)与支持向量机(SVM)分类器实现煤/岩石分类。最后，笔者进行了光谱分段测试，对特征分类性能进行优化。结果表明，所提的免校准方法，提高高光谱激光雷达直接应用能力的同时能够保持良好的分类性能。

Classification of coal/rock based on Hyperspectral LiDAR calibration-free signals

Safety is essential to deep mining operations. To monitor and detect the safety condition of surrounding rock of roadway and deep coal mining site, many methods obtain competitive results by monitoring targets situation based on laser scanning spacial information. The Hyperspectral LiDAR (HSL) technology can acquire spacial and spectral data for deep mine safety detection and further fine structure analysis. The exact coal/rock classification is the basis of detection and analysis. While, in on-site operation, HSL signals are susceptible to instrument attributes and environmental factors, and need calibration for further classification application. However, due to serious dust pollution in deep coal mines, conventional calibrations are hard to achieve the desired results. To address this issue, a new method was proposed to classify coal/rock without calibration. First, the new feature values, waveform entropy (WE) and joint skewness-kurtosis figure (JSKF), were extracted from coal/rock samples based on HSL measurements. Then, the coal/rock classification tests were conducted with random forest (RF) and support vector machine (SVM) classifiers. Additionally, the spectral properties of different wavebands were evaluated by spectral segmentation test and the classification performances were optimized further by selecting specific channels. The results show that the proposed method can achieve excellent classification accuracy for coal/rock without calibration.