EVOLUTIONARY TENSOR TRAIN DECOMPOSITION FOR HYPER-SPECTRAL REMOTE SENSING IMAGES

Hyper-spectral images are widely used for mapping and remote sensing of the Earth's surface. Different tensor decomposition methods have been applied for hyper-spectral image decomposition. In this study we present an evolutionary tensor train (ETT) decomposition. The ETT technique defines a combinatorial optimization model to find an optimal shape for the tensor train (TT) decomposition. The optimization model maximizes the compression ratio of the TT decomposition given an error bound. A genetic algorithm (GA) linked with the TT-SVD algorithm is applied to find the optimal shape. We adopt the ETT for the decomposition of hyper-spectral images and study the performance of the ETT with respect to the error bound. The results demonstrate the effectiveness of the proposed evolutionary tensor shape search for the the decomposition of the hyper-spectral images.