Feature extraction is a preprocessing step for hyperspectral image classification. Principal component analysis only uses the spectral information, but it does not use spatial information of a hyperspectral image. Both spatial and spectral information are used when hyperspectral image is modelled as tensor, that is, decreasing the noise on spatial dimension and reducing the dimension on a spectral dimension at the same time. However, in this model, a hyperspectral image is modelled only as a data cube. The factors affecting the spectral features of ground objects is not considered and these factors are barely distinguished. This means that further improving classification is very difficult. Therefore, a new model on hyperspectral image is proposed by the Ronghua Yan’s research team. In the new model, many factors that impact the spectral features of ground objects are synthesized as the within-class factor. The within-class factor, the class factor and the pixel spectral are selected as a mode, respectively. The pixel spectrals in the training set are modelled as a third-order tensor. The experiment results indicate that the new method improves the classification compared with the previous methods.

（Original research article "Journal of the Indian Society of Remote Sensing Vol. 47, Issue 1, pp. 91-100 (2019) https://link.springer.com/article/10.1007%2Fs12524-018-0873-0"）