期刊论文

Shan Zeng

英文

Sensors

1424-3210

2022

22

14

5333-

10.3390/s22145333

This research was funded by Hubei province Natural Science Foundation for Distinguished Young Scholars, grant NO. 2020CFA063, and funded by Excellent young and middle-aged scientific and technological innovation teams in colleges and universities of Hubei Province, grant NO. T2021009, and funded by Hubei Province Key Research and Development Program, grant NO. 2021BBA235, and funded by National Food and Strategic Reserves Administration Foundation, grant NO. LQ2018501, and funded by the National Natural Science Foundation of China, grant NO. U1833119 and 61705170.

本校为第一机构

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 ...