Fusion and registration of THEOS multispectral and panchromatic images

Abstract

This article presents a new method for the fusion and registration of THEOS (Thailand Earth Observation Satellite) multispectral and panchromatic images in a single step. In the usual procedure, fusion is an independent process separated from the registration process. However, both image registration and fusion can be formulated as estimation problems. Hence, the registration parameters can be automatically tuned so that both fusion and registration can be optimized simultaneously. Here, we concentrate on the relationship between low-resolution multispectral and high-resolution panchromatic imagery. The proposed technique is based on a statistical framework. It employs the maximum a posteriori (MAP) criterion to jointly solve the fusion and registration problem. Here, the MAP criterion selects the most likely fine resolution multispectral and mapping parameter based on observed coarse resolution multispectral and fine resolution panchromatic images. The Metropolis algorithm was employed as the optimization algorithm to jointly determine the optimum fine resolution multispectral image and mapping parameters. In this work, a closed-form solution that can find the fused multispectral image with correcting registration is also derived. In our experiment, a THEOS multispectral image with high spectral resolution and a THEOS panchromatic image with high spatial resolution are combined to produce a multispectral image with high spectral and spatial resolution. The results of our experiment show that the quality of fused images derived directly from misaligned image pairs without registration error correction can be very poor (blurred and containing few sharp edges). However, with the ability to jointly fuse and register an image pair, the quality of the resulting fused images derived from our proposed algorithm is significantly improved, and, in the simulated cases, the fused images are very similar to the original high resolution multispectral images, regardless of the initial registration errors.

Acknowledgements

The authors thank the three anonymous reviewers for their valuable comments and suggestions.

Funding

This work is supported in part by the Thailand Research Fund [grant number RSA5480031].

Appendix

A. Derivation of Equation (17) From Equation (16), the MAP equation can be written as

(A1)

where

(A2)

Equation (A2) can be modified to

(A3)

where is the remaining term that is independent of . Next, we define

(A4)

and

(A5)

By substituting Equations (A4) and (A5) into Equation (A3), we then obtain

(A6)

where is a constant and independent of . From Equation (A6), the MAP equation becomes

(A7)

Since is independent of , it can be removed and the MAP equation is given by

(A8)