棉花杂质在轧棉过程中对棉纤维造成损伤, 导致成品纺织品出现瑕疵。 因此, 杂质的检测和分类在棉花生产过程及质量检验中至关重要。 地膜是我国机采棉中特有的杂质。 该研究将包括地膜碎片等12种常见棉花杂质放置于两层皮棉层之间, 采用推扫式高光谱成像系统在透射模式下对杂质与皮棉混合样本进行图像采集, 在400~1 000 nm范围内利用光谱信息识别嵌在皮棉层中的12种杂质。 首先对高光谱图像进行平场校正, 对边缘噪声进行裁剪； 选择500 nm处灰度图像进行人工感兴趣区域（ROIs）提取, 从ROIs提取皮棉和杂质平均透射光谱并进行标准化； 使用典型判别分析（CDA）对皮棉和杂质光谱进行处理并利用前三个典型变量绘制散点图, 观察散点分组情况, 采用多变量方差分析（MANOVA）对前三个典型变量评估每两种杂质之间的差异。 然后使用区间随机蛙跳（iRF）方法提取特征波段, 采用支持向量机（SVM）分类器, 分别对全波段及特征波段的透射光谱进行杂质和皮棉13个类别的分类研究, 对比分析两次分类的准确率。 结果表明, 全波段的各类杂质和皮棉的平均分类准确率为84.4%, 该方法对棉花内层杂质的检测与分类是可行的, 包括与皮棉外观相近的地膜、 塑料包装和纸的分类效果较好。 在提取12个特征波段后, 4种具有相似外观和相似化学成分的杂质（裂茎、 茎皮、 棉铃壳、 棕叶）分类准确率较低但都超过73%； 棉籽、 绿叶、 纸片、 塑料包装、 地膜、 皮棉的分类准确率均超过90%； 各类杂质和皮棉的平均分类准确率为86.2%； 与全波段光谱的分类结果相比, 特征波段光谱的平均分类准确率提高1.8%。 该研究结果可为棉花内层杂质检测研究提供理论依据, 并对高光谱透射成像技术的应用有较好的指导作用。

Cotton foreign matter (FM) harms fiber quality as it may damage cotton fiber during ginning processing or cause flaws in finished textiles. Therefore, detecting and classifying foreign matter are important in the cotton production process and quality assessment. The mulching film is a unique impurity in machine-harvested seed cotton in China. Since the mulching film is commonly used to grow cotton in Xinjiang, the remaining fragments are mixed into cotton during mechanical harvesting. This study placed 12 types of common cotton foreign matter, including mulching film fragments, between two lint layers. A push-broom-based hyperspectral imaging system was used to acquire images of the mixed foreign matter and lint samples in transmittance mode at the spectral range of 400~1 000 nm. The hyperspectral transmittance images were first corrected using flat-field correction and cropped due to noise at the edges. The images at 500 nm were chosen for manual region-of-interest (ROI) selection. Mean transmittance spectra were extracted from the ROIs and normalized across all samples. Canonical discriminant analysis (CDA) and the first three canonical variables were used to group foreign matter and lint, and multivariate analysis of variance (MANOVA) was employed to evaluate the differences between each combination of two types of foreign matter using the first three canonical variables. Then, the interval Random Frog (iRF) method was used to extract 12 feature wavelengths. A support vector machine (SVM) classifier was used to classify the transmittance spectra of full and selected wavelengths respectively, and the accuracies were compared and analyzed. The results show that the average classification accuracy of all types of foreign matter and lint at the full wavelength was 84.4%. The method in this paper was feasible for classifying foreign matter in the inner layer of cotton, including plastic packaging, paper, and mulching film. After extracting the feature wavelengths, the classification accuracy of 4 types of foreign matter with similar appearance and similar chemical composition (broken stem, hull, bark, brown leaf) was lower, but all exceeded 73%. The classification accuracy of seed meat, green leaf, paper, plastic package, mulching film, and lint was over 90%. The average classification accuracy of all foreign matter and lint types was 86.2%. Compared with the classification results of the full-wavelength, the average classification accuracy of the selected wavelength was improved by 1.8%.The results of this study can provide a theoretical basis for the research on the detection of foreign matter in the inner layer of cotton and have a guiding role for the application of hyperspectral transmittance imaging technology.