关键词: 高光谱图像技术, 多维尺度分析, 在线序列极限学习机, 极限学习机, 多层感知机

Abstract: To meet the demand on rapid and damage-free rice origin confirmation, hyper-spectral images(400~1 000 nm) of a total number of 990 rice samples from primary rice production regions of Meihekou city, Jilin province, as well as other rice producing regions such as Songyuan, Daan and Huinan, were collected as the object of study, and the spectra were preprocessed by multiplicative scatter correction (MSC) handling method. With the adoption of multilayer perceptro (MLP), extreme learning machine (ELM) and online sequence extreme learning machine (OS-ELM) algorithms, the origin confirmation model was established on the basis of the full-band hyperspectral data and the data undergoing dimensionality reduction by the multidimensional scaling (MDS) method. The results indicated that the OS-ELM model based on full-band hyperspectral data had the best classification performance with an accuracy rate of 98.3%. After the MDS processing, the input data variables were reduced by 96.6%, while the accuracy rate of the MDS-OS-ELM model was stable at 97.4%. The comparative analysis was performed against the training time among three models, and it was concluded that the training time of OS-ELM was significantly more than that of MLP, and the training time on data acquisition in batches was more than ELM, which provided an efficient, accurate and stable method for rice origin confirmation.

Key words: hyper-spectral, multidimensional scaling, extreme learning machine, online sequential-extreme learning machine, multi-layer perceptron