Weighted manifold regularized sparse representation of featured injected details for pansharpening

ABSTRACT

Sparse representation (SR)-based pansharpening methods which combine the dictionary and estimated sparse coefficients have achieved visually and quantitatively great results in pansharpening problem these days. And the details injection (ID)-based methods can receive comparable images by sharpening the multispectral bands through adding the proper spatial details from panchromatic (PAN) images. Recently, method based on sparse representation of injected details (SR-D) which combines the SR and ID points out a new way forward for pansharpening. In this direction, manifold regularized sparse representation of injected details (MR-SR-D) which introducing a manifold regularization (MR) into the former SR-D model have improved the quality of pansharpened images greatly utilizing a graph Laplacian to incorporate the locally geometrical structure of the multispectral data. However, due to the lack of spatial information in PAN, and the use of higher-order features similarity with the original multispectral images, the resulting images still have spatial and spectral distortion. Thus, in this paper, we propose a new method to enhance the spatial resolution of aiming image by adding the weighted local geometrical structure of PAN and multispectral images, and improve the spectral resolution by joining the higher-order structure connection between multispectral and aiming images to the MR-SR-D method which can be called as weighted manifold regularized (WMR) sparse representation of featured injected details method (WMR-SR-FD). Experimental results using IKONOS, QuickBird and WorldView2 data show that the proposed method can achieve remarkable spectral and spatial quality.

Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions, which led to a substantial improvement of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. http://openremotesensing.net/

Additional information

Funding

This work is supported by the National Key Research and Development Program under Grant Nos. 2018AAA0102201, and National Science Foundation of China under Grant Nos. 61877049, 11671317, 61976174.