School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430048, China.
Sensors (Basel). 2022 Jul 17;22(14):5333. doi: 10.3390/s22145333.
Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality. Supervised learning is the mainstream method of hyperspectral imaging for pixel-level detection of mildew in corn kernels, which requires a large number of training samples to establish the prediction or classification models. This paper presents an unsupervised redundant co-clustering algorithm (FCM-SC) based on multi-center fuzzy c-means (FCM) clustering and spectral clustering (SC), which can effectively detect non-uniformly distributed mildew in corn kernels. This algorithm first carries out fuzzy c-means clustering of sample features, extracts redundant cluster centers, merges the cluster centers by spectral clustering, and finally finds the category of corresponding cluster centers for each sample. It effectively solves the problems of the poor ability of the traditional fuzzy c-means clustering algorithm to classify the data with complex structure distribution and the complex calculation of the traditional spectral clustering algorithm. The experimental results demonstrated that the proposed algorithm could describe the complex structure of mildew distribution in corn kernels and exhibits higher stability, better anti-interference ability, generalization ability, and accuracy than the supervised classification model.
高光谱成像是一种可以同时获取样品光谱和空间信息的技术，因此被广泛应用于粮食质量的无损检测中。监督学习是高光谱成像用于玉米籽粒霉变像素级检测的主流方法，它需要大量的训练样本来建立预测或分类模型。本文提出了一种基于多中心模糊 c 均值（FCM）聚类和光谱聚类（SC）的无监督冗余协同聚类算法（FCM-SC），可以有效地检测玉米籽粒中不均匀分布的霉变。该算法首先对样本特征进行模糊 c 均值聚类，提取冗余聚类中心，通过光谱聚类合并聚类中心，最后为每个样本找到对应的聚类中心类别。该算法有效解决了传统模糊 c 均值聚类算法对复杂结构分布数据分类能力差和传统光谱聚类算法计算复杂的问题。实验结果表明，该算法能够描述玉米籽粒中霉变分布的复杂结构，并且具有比监督分类模型更高的稳定性、更好的抗干扰能力、泛化能力和准确性。
