Yang Sai, Zhu Qi-bing, Huang Min
Guang Pu Xue Yu Guang Pu Fen Xi. 2017 Mar;37(3):990-6.
As an effective method for the nondestructive measurement of agricultural products quality, hyperspectral imaging technology has been widely studied in the field of seed classification and identification. Feature extraction and optimal wavelength selection are the two critical issues affecting the application of hyperspectral image in the field of seed identification. This study aimed to select optimal wavelengths from hyperspectral image data using joint skewness algorithm, so that they can be deployed in multispectral imaging-based inspection system for the automatic classification of maize seed. The hyperspectral images covering the wavelength range of 438～1 000 nm were acquired for 960 maize seeds including 10 varieties. After extracting the mean spectrum and entropy from the hyperspectral images, the joint skewness algorithm was used to select optimal wavelengths, and the classification models based on support vector machine were developed using the mean spectrum, entropy, and their combination, respectively. The experimental results indicated that the classification accuracy of the models developed by combination of the mean spectrum and entropy were higher than that of the mean spectrum or entropy for either full wavelengths or optimal wavelengths. The classification model for the combination of the mean spectrum and entropy based on the 10 optimal wavelengths selected by the joint skewness algorithm obtained 96.28% accuracy for test samples, with improvements of 4.30% and 20.38% over that of the mean spectrum and entropy, respectively, which was higher than the classification accuracy of the model that developed in the full wavelength (i.e., 93.47%). Meanwhile, the classification model based on joint skewness algorithm yielded the better classification accuracy than that of uninformative viable elimination algorithm, successive projections algorithm, and competitive adaptive reweighed sampling algorithm. This study made the online application of the hyperspectral image technology available for seed identification.
作为一种用于农产品质量无损检测的有效方法，高光谱成像技术在种子分类与识别领域得到了广泛研究。特征提取和最优波长选择是影响高光谱图像在种子识别领域应用的两个关键问题。本研究旨在利用联合偏度算法从高光谱图像数据中选择最优波长，以便将其应用于基于多光谱成像的检测系统中对玉米种子进行自动分类。采集了覆盖438～1000nm波长范围的960粒玉米种子（包括10个品种）的高光谱图像。从高光谱图像中提取平均光谱和熵后，使用联合偏度算法选择最优波长，并分别基于平均光谱、熵及其组合建立支持向量机分类模型。实验结果表明，无论是全波长还是最优波长，基于平均光谱和熵组合建立的模型的分类准确率均高于仅基于平均光谱或熵建立的模型。基于联合偏度算法选择的10个最优波长的平均光谱和熵组合的分类模型对测试样本的准确率达到96.28%，分别比仅基于平均光谱和熵的模型提高了4.30%和20.38%，高于全波长模型的分类准确率（即93.47%）。同时，基于联合偏度算法的分类模型比无信息变量消除算法、连续投影算法和竞争性自适应重加权采样算法具有更好的分类准确率。本研究使高光谱图像技术可在线应用于种子识别。
