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SIRF: Simultaneous Satellite Image Registration and Fusion in a Unified Framework.

Authors
Type
Published Article
Journal
IEEE Transactions on Image Processing
1941-0042
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Volume
24
Issue
11
Pages
4213–4224
Identifiers
DOI: 10.1109/TIP.2015.2456415
PMID: 26186776
Source
Medline
License
Unknown

Abstract

In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral (Ms) image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares fitting term and a dynamic gradient sparsity regularizer. The former is to preserve accurate spectral information of the Ms image, while the latter is to keep sharp edges of the high-resolution panchromatic image. We further propose to simultaneously register the two images during the fusing process, which is naturally achieved by virtue of the dynamic gradient sparsity property. An efficient algorithm is then devised to solve the optimization problem, accomplishing a linear computational complexity in the size of the output image in each iteration. We compare our method against six state-of-the-art image fusion methods on Ms image data sets from four satellites. Extensive experimental results demonstrate that the proposed method substantially outperforms the others in terms of both spatial and spectral qualities. We also show that our method can provide high-quality products from coarsely registered real-world IKONOS data sets. Finally, a MATLAB implementation is provided to facilitate future research.

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