## 1 Introduction  
Rice is one of the most common food crops planted in China and some other countries. Rice seeds are harvested as food to consumers and as seed for the following sowing season. Due to the vast planting areas across villages, towns, countries and continents, different varieties of rice seeds are developed to adapt to changes of growth environments (climates, soil, water, etc.) and improve nutrition and flavors. With the development of breeding techniques, more varieties of rice seeds are brought into the market, and the purity of rice seeds is critical for planners and consumers. Different varieties of rice seed vary in physical and chemical characteristics, growth performances and stress tolerance. Rice seed varieties can be identified by inspecting the appearance characteristics such as size, color, shape and texture, or by determining the quality attributes such as protein, starch and aroma. Traditional methods for rice variety identification, like High Performance Liquid Chromatography (HPLC) and Gas Chromatography-Mass Spectrometer (GC-MS), are either time consuming or expensive, and generally are only applied on a small number of sampled seeds [1,2]. Thus, developing a rapid method to identify rice seed varieties is needed. Hyperspectral imaging is a technique integrating visible/near-infrared spectroscopy and imaging to acquire spectral and spatial information of the samples simultaneously. A hyperspectral image can be defined as a data cube of shape \(w\times h\times b\), where \(w\) (width) and \(h\) (height) denote the spatial resolution and \(\mathbf{b}\) denotes the number of spectral bands. From the perspective of imaging, there is an image of size \(w\times h\) for each spectral band. From the perspective of spectroscopy, there is a spectrum of length \(b\) for each spatial pixel. Compared with non-imaging spectroscopy, it is possible to obtain spectra of multiple samples in one single scan using a hyperspectral imaging system. There are hyperspectral imaging systems for different spectral ranges, and identification of seed varieties has been conducted at different spectral ranges [3; 4; 5; 6]. Hyperspectral imaging generates a large amount of data representing spectral and spatial information. Multivariate hyperspectral image analysis is required to deal with such amount of data based on different objectives, including spectral and image preprocessing, feature selection and extraction, regression and classification analysis [7; 8; 9; 10]. Classification methods have been successfully used to identify seed varieties using hyperspectral imaging [11] and various modeling methods were used. [PERSON] et al. (2012) used a line scan hyperspectral imaging system to identify maize varieties using batches of maize kernels. Principal component analysis (PCA) and kernel PCA (KPCA) were applied to extract useful information from spectra, then least squares support vector machine (LS-SVM) and back-propagation neural network (BPNN) were used for classification. The classification accuracy was all over 90% [3]. [PERSON] et al. (2013) used a near-infrared hyperspectral imaging system to classify rice seed varieties. The key point was to compare the performances of different discriminant models, including partial least square discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbor (KNN), support vector machine (SVM) and random forest (RF). The classification accuracy of most discriminant models was over 90% [6]. [PERSON] et al. (2013) used a near-infrared hyperspectral imaging system at the range of 960-1700 nm to classify 4 wheat classes. Particularly, seeds were collected with variations of crop year, growing location and water content. Seed classifications were conducted based on water content of 13%, 16% and 19%. The pairwise moisture specific models (linear discriminant analysis, quadratic discriminant analysis and BPNN) were built with good classification results.