๐
๐
Old Age
A Survey of Pansharpening Methods with A New Band-Decoupled Variational Model
June 17, 2016 ยท The Cartographer ยท ๐ arXiv.org
"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Survey of Pansharpening Methods with A New Band-Decoupled Variational Model"
Evidence collected by the PWNC Scanner
Authors
Joan Duran, Antoni Buades, Bartomeu Coll, Catalina Sbert, Gwendoline Blanchet
arXiv ID
1606.05703
Category
cs.CV: Computer Vision
Cross-listed
math.OC
Citations
87
Venue
arXiv.org
Last Checked
1 day ago
Abstract
Most satellites decouple the acquisition of a panchromatic image at high spatial resolution from the acquisition of a multispectral image at lower spatial resolution. Pansharpening is a fusion technique used to increase the spatial resolution of the multispectral data while simultaneously preserving its spectral information. In this paper, we consider pansharpening as an optimization problem minimizing a cost function with a nonlocal regularization term. The energy functional which is to be minimized decouples for each band, thus permitting the application to misregistered spectral components. This requirement is achieved by dropping the, commonly used, assumption that relates the spectral and panchromatic modalities by a linear transformation. Instead, a new constraint that preserves the radiometric ratio between the panchromatic and each spectral component is introduced. An exhaustive performance comparison of the proposed fusion method with several classical and state-of-the-art pansharpening techniques illustrates its superiority in preserving spatial details, reducing color distortions, and avoiding the creation of aliasing artifacts.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Computer Vision
๐
๐
Old Age
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
๐
๐
Old Age
SSD: Single Shot MultiBox Detector
๐
๐
Old Age
Squeeze-and-Excitation Networks
๐
๐
Old Age
Fast R-CNN
๐
๐
Old Age