Hyperspectral pansharpening based on homomorphic filtering and weighted tensor matrix

9Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.

Abstract

Hyperspectral pansharpening is an effective technique to obtain a high spatial resolution hyperspectral (HS) image. In this paper, a new hyperspectral pansharpening algorithm based on homomorphic filtering and weighted tensor matrix (HFWT) is proposed. In the proposed HFWT method, open-closing morphological operation is utilized to remove the noise of the HS image, and homomorphic filtering is introduced to extract the spatial details of each band in the denoised HS image. More importantly, a weighted root mean squared error-based method is proposed to obtain the total spatial information of the HS image, and an optimized weighted tensor matrix based strategy is presented to integrate spatial information of the HS image with spatial information of the panchromatic (PAN) image. With the appropriate integrated spatial details injection, the fused HS image is generated by constructing the suitable gain matrix. Experimental results over both simulated and real datasets demonstrate that the proposed HFWT method effectively generates the fused HS image with high spatial resolution while maintaining the spectral information of the original low spatial resolution HS image.

Cite

CITATION STYLE

APA

Qu, J., Li, Y., Du, Q., Dong, W., & Xi, B. (2019). Hyperspectral pansharpening based on homomorphic filtering and weighted tensor matrix. Remote Sensing, 11(9). https://doi.org/10.3390/rs11091005

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free