Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix

Jiahui Qu,Qian Du,Bobo Xi,Wenqian Dong,Yunsong Li

doi:10.3390/rs11091005

Jiahui Qu, Qian Du + Show 3 more

Open Access

https://doi.org/10.3390/rs11091005

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Journal: Remote Sensing	Publication Date: Apr 27, 2019
Citations: 7	License type: CC BY 4.0

Affiliation: Mississippi State University, Xidian University

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.

Highlights

Depending on the number of acquired bands, remote sensing imaging technology has developed from collecting panchromatic (PAN) and color images to multispectral (MS) images, and it can capture hyperspectral (HS) images with dozens of hundreds of bands
A weighted root mean squared error based method is proposed to obtain the total spatial component of the LRHS image from extracted spatial details of each band, and the Laplacian pyramid networks super-resolution algorithm is adopted to enhance the spatial resolution of the obtained spatial component
Let the LRHS image be represented by XHLRS ∈ Rm×n×B, and the HRPAN image be denoted by XPAN ∈ RM×N×1, where m × n and M × N are the size of the LRHS and the HRPAN images, respectively, and B is the number of the LRHS image bands

Summary

Introduction

Depending on the number of acquired bands, remote sensing imaging technology has developed from collecting panchromatic (PAN) and color images to multispectral (MS) images, and it can capture hyperspectral (HS) images with dozens of hundreds of bands. Due to the incomplete spatial information injection, the CS and MRA approaches may result in distortion To address this problem, we propose a novel hyperspectral pansharpening method by combining homomorphic filtering with a weighted tensor matrix. An optimized weighted tensor matrix-based method which considers the structure information of the LRHS and HRPAN images is proposed to generate more comprehensive spatial information. A weighted root mean squared error based method is proposed to obtain the total spatial component of the LRHS image from extracted spatial details of each band, and the Laplacian pyramid networks super-resolution algorithm is adopted to enhance the spatial resolution of the obtained spatial component. A new hyperspectral pansharpening method based on homomorphic filtering and weighted tensor matrix is proposed in this paper.

Related Work

Homomorphic Filtering

Hyperspectral Image Preprocessing

Hyperspectral Image Spatial Information Extraction

Validity Discussion of the Open-Closing Denoising Operation