Next Article in Journal
Objective Classes for Micro-Facial Expression Recognition
Next Article in Special Issue
Age Determination of Blood-Stained Fingerprints Using Visible Wavelength Reflectance Hyperspectral Imaging
Previous Article in Journal
Multivariate Statistical Approach to Image Quality Tasks
Previous Article in Special Issue
Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
J. Imaging 2018, 4(10), 118; https://doi.org/10.3390/jimaging4100118

Fusing Multiple Multiband Images

Commonwealth Scientific and Industrial Research Organisation (CSIRO), Pullenvale QLD 4069, Australia
Received: 21 August 2018 / Revised: 5 October 2018 / Accepted: 8 October 2018 / Published: 12 October 2018
(This article belongs to the Special Issue The Future of Hyperspectral Imaging)
Full-Text   |   PDF [4646 KB, uploaded 15 October 2018]   |  

Abstract

High-resolution hyperspectral images are in great demand but hard to acquire due to several existing fundamental and technical limitations. A practical way around this is to fuse multiple multiband images of the same scene with complementary spatial and spectral resolutions. We propose an algorithm for fusing an arbitrary number of coregistered multiband, i.e., panchromatic, multispectral, or hyperspectral, images through estimating the endmember and their abundances in the fused image. To this end, we use the forward observation and linear mixture models and formulate an appropriate maximum-likelihood estimation problem. Then, we regularize the problem via a vector total-variation penalty and the non-negativity/sum-to-one constraints on the endmember abundances and solve it using the alternating direction method of multipliers. The regularization facilitates exploiting the prior knowledge that natural images are mostly composed of piecewise smooth regions with limited abrupt changes, i.e., edges, as well as coping with potential ill-posedness of the fusion problem. Experiments with multiband images constructed from real-world hyperspectral images reveal the superior performance of the proposed algorithm in comparison with the state-of-the-art algorithms, which need to be used in tandem to fuse more than two multiband images. View Full-Text
Keywords: alternating direction method of multipliers; Cramer–Rao lower bound; forward observation model; linear mixture model; maximum likelihood; multiband image fusion; total variation alternating direction method of multipliers; Cramer–Rao lower bound; forward observation model; linear mixture model; maximum likelihood; multiband image fusion; total variation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Arablouei, R. Fusing Multiple Multiband Images. J. Imaging 2018, 4, 118.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top