Hyperspectral image classification using sub-band tensor factorization and convolutional neural network

Document Type : Research Paper


1 School of Engineering Science, College of Engineering, University of Tehran

2 Satellite Systems Research Institute, Iran Space Research Center


In this paper, we are going to classify each pixel of a hyperspectral image. For this purpose, we group the spectral bands to sub-bands and try to decompose the corresponding sub-tensors to the endmember and abundance matrices. Abundance matrices obtained through tensor factorization methods contain spatial information in contrast to the ones acquired by matrix factorization. Therefore, the 2D abundance maps achieved by tensor decomposition methods, construct discriminant features for the classifier. A 3D CNN architecture is proposed for classification which utilizes the abundance maps of the individual sub-bands as input features. This way, we jointly exploit spectral and spatial information of the image. The experiments are performed on well-known hyperspectral data and reveal the effectiveness of the proposed sub-band tensor decomposition methods compared to matrix factorization approaches.