Hyperspectral image fusion based on non-factorization sparse representation and error matrix estimation

Matrix factorization with non-negative constrains is widely used in hyperspectral image fusion. Nevertheless, the non-negative restriction on the sparse coefficients limits the efficiency of dictionary representation. To solve this problem, a new hyperspectral image fusion method based on non-factorization sparse representation and error matrix estimation is proposed in this paper, for the fusion of remotely sensed high-spatial multi-bands image with low-spatial hyperspectral image in the same scene. Firstly, an efficient spectral dictionary learning method is specifically adopted for the construction of the spectral dictionary, which avoids the procedure of matrix factorization. Then, the sparse codes of the high-spatial multi-bands image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers (ADMM) without non-negative constrains. For improving the quality of final fusion result, an error matrix estimation method is also proposed, exploiting the spatial structure information after non-factorization sparse representation. Experimental results both on simulated and real datasets demonstrate that, compared with the related state-of-the-art methods, our proposed method achieves the highest quality of hyperspectral image fusion, which can improve PSNR over 2.5844 and SAM over 0.3758.

1. Introduction

High-spatial hyperspectral images have been widely used in various fields, such as environment monitoring, precision agriculture, military detective and so on. Nevertheless, the increasing of spectral bands in hyperspectal imaging, leads to enlarging the size of the photosensitive elements, which results in the limitations on spatial resolution [1]. Normally, the spatial information lost in hyperspectral imaging, can be obtained by fusing high-spatial multi-bands images through some kind of post processing.