Hyperspectral Mineral Mapping Method Based on Spectral Characteristic Parameter Combination
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摘要: 受大气环境等因素的影响, 高光谱遥感矿物识别难以达到较高的精度.为解决该问题, 根据光谱吸收特征参数在大气变化中能保持相对稳定的特点, 提出一种基于光谱特征参数组合的高光谱矿物类型识别方法.文中计算了多种光谱特征参数, 通过最佳指数因子(optimum index factor, OIF)优选特征参数组合, 选定最佳特征参数组合, 利用模式识别方法实现矿物识别.利用机载可见/红外成像光谱仪(airborne visible infrared imaging spectrometer, AVIRIS)高光谱数据, 在美国内华达州Cuprite矿区进行了该方法的应用试验研究, 并与前人矿物填图结果做了对比.结果表明: 吸收波谷位置-吸收面积-吸收右肩位置(P-A-S2)光谱特征参数组合的矿物识别效果最优, 整体精度达到74.68%.Abstract: Influenced by the atmospheric environment and other factors, the mineral recognition with hyperspectral remote sensing is difficult to achieve a high accuracy. To improve the accuracy of the mineral identification with such technology, a hyperspectral mineral recognition method based on spectral characteristic parameter combination, which can maintain relatively stable characteristics with the atmospheric changes, is proposed in this paper. Various spectral characteristic parameters are calculated, and the optimal combination of the parameters is selected through the optimum index factor (OIF), based on which, mineral identification is realized with pattern recognition method. Based on the above method, mineral type identification test is carried out in Cuprite mine of Nevada, with airborne visible infrared imaging spectrometer (AVIRIS) hyperspectral data. The results are compared with the work of previous mineral mapping, it shows that the combination of the spectral characteristic parameters, P-A-S2 (P is absorption wave trough position, A is absorption area, S2 is absorption right shoulder position) can get the highest identification precision, the overall accuracy can reach 74.68%.
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图 1 AVIRIS数据大气校正前后的光谱曲线
a为大气校正前；b为大气校正后
Fig. 1. Spectrum after atmospheric correction of AVIRIS data
图 2 USGS在Cuprite矿区的矿物填图结果
Fig. 2. Mineral mapping result in the Cuprite mining area made by USGS
图 3 光谱吸收特征参数示意
Fig. 3. The schematic diagram of spectral absorption characteristic parameters
图 4 光谱特征参数影像假彩色合成影像
a为W-S-S2彩色合成影像；b为P-A-S2彩色合成影像
Fig. 4. False color composition image of characteristic parameters image
图 5 不同光谱特征参数组合的Cuprite矿区矿物填图结果
a为W-S-S2组合矿物填图结果；b为P-A-S2组合矿物填图结果
Fig. 5. Mineral mapping results under different spectral characteristic parameters combination in Cuprite
表 1 AVIRIS光谱特征参数影像OIF计算结果
Table 1. The OIF calculation result of AVIRIS spectral characteristic parameters
排名 特征参数1 特征参数2 特征参数3 OIF 1 W S S2 6 529.205 4 2 P A S2 3 166.070 6 3 P W S2 1 869.845 3 4 P K S2 1 658.919 5 5 W H S2 1 179.317 2 6 P R K 728.601 90 7 W A SAI 520.429 52 8 P K S1 321.255 93 9 K S1 S2 294.259 42 10 W H K 258.377 96 
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表 2 不同光谱特征参数组合的矿物填图精度对比分析
Table 2. Classification accuracies of mineral mapping for different spectral characteristic parameters combination
精度评价 W-S-S2组合 P-A-S2组合 生产者精度(%) 用户精度(%) 生产者精度(%) 用户精度(%) 明矾石 58.48 64.47 67.58 74.66 高岭石 74.84 66.06 71.49 71.17 白云母 88.73 97.57 90.23 93.55 蒙脱石 18.16 21.82 33.43 19.06 方解石 89.94 85.02 88.95 84.72 Kappa系数 0.580 5 0.651 9 整体精度(%) 70.07 74.68
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