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Open AccessArticle

An Efficient Compressive Hyperspectral Imaging Algorithm Based on Sequential Computations of Alternating Least Squares

Division of Global Business and Technology, Hankuk University of Foreign Studies, Yongin 17035, Korea
Remote Sens. 2019, 11(24), 2932; https://doi.org/10.3390/rs11242932
Received: 23 October 2019 / Revised: 28 November 2019 / Accepted: 3 December 2019 / Published: 6 December 2019
Hyperspectral imaging is widely used to many applications as it includes both spatial and spectral distributions of a target scene. However, a compression, or a low multilinear rank approximation of hyperspectral imaging data, is required owing to the difficult manipulation of the massive amount of data. In this paper, we propose an efficient algorithm for higher order singular value decomposition that enables the decomposition of a tensor into a compressed tensor multiplied by orthogonal factor matrices. Specifically, we sequentially compute low rank factor matrices from the Tucker-1 model optimization problems via an alternating least squares approach. Experiments with real world hyperspectral imaging revealed that the proposed algorithm could compute the compressed tensor with a higher computational speed, but with no significant difference in accuracy of compression compared to the other tensor decomposition-based compression algorithms. View Full-Text
Keywords: hyperspectral imaging; HOSVD; alternating least squares hyperspectral imaging; HOSVD; alternating least squares
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Lee, G. An Efficient Compressive Hyperspectral Imaging Algorithm Based on Sequential Computations of Alternating Least Squares. Remote Sens. 2019, 11, 2932.

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