针对当前利用高光谱数据进行矿物识别精度较低的问题, 根据研究区地质背景建立区域端元光谱库, 提出了对原始光谱进行分段滤波的预处理方法。 首先应用连续统快速傅里叶变换方法分别去除2 000～2 200 nm, 2 250～2 300 nm, 2 350 ～2 500 nm范围内的随机噪声, 之后利用加入区域端元库的矿物快速定量提取模型提取预处理后光谱中的矿物类型。 本方法识别矿物的最高有效率为80%, 正确率最高可达67%。 与未滤波的光谱识别结果对比, 平均正确率提高了17.7%, 平均有效率提高了5.1%; 与全波段滤波的光谱识别结果对比, 平均正确率提高了5.8%, 平均有效率提高了39.8%, 可保证在尽量多识别出正确矿物的基础上有效减少结果中的错误组分数, 改进了矿物识别的精度, 对野外快速提取矿物信息等工作有重要意义。

Aiming at the low accuracy of mineral identification with hyperspectral data, the present article established regional spectra library on the basis of the study area geological background, and presented a pretreatment method that filters the original spectra by section. First, continuum based fast Fourier transform was used to filter the noise among 2 000~2 200, 2 250~2 300 and 2 350~2 500 nm. Then apply the Rapid quantificational identification model with regional spectrum library was used to dispose the processed spectra. The highest effective rate of the result is 80%, and the highest accuracy rate is 67%. Compared with the identification result of original spectra, the average accuracy rate was upgraded by 17.7%, and the average effective rate was upgraded by 5.1%. Compared with the identification result of all-filtered spectra, the average accuracy rate was upgraded by 5.8%, while the average effective rate was upgraded by 39.8%. This method , which could guarantee that the identification result contains the most correct minerals and the fewest error ones, promoted mineral identification accuracy. The result with higher accuracy is significant to rapid mineral extraction work in field.