Abstract Precise classification of seeds is important for agriculture. Due to the slight physical and chemical difference between different types of wheat and high correlation between bands of images, it is easy to fall into the local optimum when selecting the characteristic band of using the spectral average only. In this paper, in order to solve this problem, a new variable fusion strategy was proposed based on successive projection algorithm and the variable importance in projection algorithm to obtain a comprehensive and representative variable feature for higher classification accuracy, within spectral mean and spectral standard deviation, so the 25 feature bands obtained are classified by support vector machine, and the classification accuracy rate reached 83.3%. It indicates that the new fusion strategy can mine the effective features of hyperspectral data better to improve the accuracy of the model and it can provide a theoretical basis for the hyperspectral classification of tiny kernels.

中文翻译：

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一种利用高光谱数据区分种子菌株的改进特征融合方法

摘要 种子的精确分类对农业具有重要意义。由于不同类型小麦的物理化学差异很小，图像波段之间的相关性较高，仅使用光谱平均值选择特征波段时容易陷入局部最优。针对这一问题，本文提出了一种基于逐次投影算法和投影算法中变量重要性的新的变量融合策略，在光谱均值和光谱标准偏差范围内，获得综合的、具有代表性的变量特征，从而获得更高的分类精度。 ，所以得到的25个特征带通过支持向量机进行分类，分类准确率达到83.3%。