自然界中不同种物质拥有不同的偏振特性, 这些特征信号能用于检测不同的目标地物。为了探索偏振光谱技术用于精确识别作物和杂草的可行性, 此研究利用配置偏振片的成像光谱仪FISS-P在室内采集玉米与5种杂草的偏振光谱影像。通过比较和分析0°、60°、120°和无偏4种状态下玉米与各种杂草的光谱响应规律、光谱特征和决策识别模型精度, 结果显示4种偏振状态下玉米和杂草的光谱变化趋势较一致, 无偏状态下玉米和杂草的光谱强度最大; 不同偏振状态下玉米和杂草的敏感波段既存在共性又表现出一定的差异性; 4种偏振状态下玉米杂草识别模型的总体精度和Kappa系数均达到90%以上, 其中, 0°偏振状态下玉米和杂草识别模型的整体精度最高, 接近100%。综上, 偏振光谱能够在叶片尺度较好地识别玉米和杂草, 这为田间尺度进一步应用提供了扎实的数据积累。

In order to explore the feasibility of accurate identification between crop and weed species using polarization spectroscopy, Field Imaging Spectral System (FISS) was utilized with a polaroid configuration to collect imagery data of corn and five kinds of weeds in the laboratory. Through comparisons and analysis of spectral response curves, characteristic difference and identification model accuracy between corn and weeds under four polarization angles, it was found that there was a consistency for spectral changing trends between corn and five kinds of weeds, and the spectral intensity of corn and weeds displayed highest in the no polarization status. Moreover, the selected sensitive bands under four polarization conditions to distinguish corn and weed species indicated that there were similar characteristics, as well as some differences. Finally, for overall accuracy of the identification models between corn and weeds, and the corresponding Kappa coefficients were all more than 90%. The accuracy was the highest, close to 100%, when data were measured at 0° polarization angle. Therefore, polarization technology can be used to identify corn and weeds on the leaf scale, providing an important data foundation for further application on a field scale.