ML Anthology

Nonrigid Registration of Hyperspectral and Color Images with Vastly Different Spatial and Spectral Resolutions for Spectral Unmixing and Pansharpening

Yuan Zhou, Anand Rangarajan, Paul D. Gader
CVPRW 2017 pp. 1571-1579
doi:10.1109/CVPRW.2017.201 /cvprw/2017/zhou2017cvprw-nonrigid/

Abstract

In this paper, we propose a framework to register images with very large scale differences by utilizing the point spread function (PSF), and apply it to register hyperspectral and hi-resolution color images. The algorithm minimizes a least-squares (LSQ) objective function with an incorporated spectral response function (SRF), a nonrigid freeform deformation applied on the hyperspectral image and a rigid transformation on the color image. The optimization problem is solved by updating the two transformations and the two physical functions in an alternating fashion. We executed the framework on a simulated Pavia University dataset and a real Salton Sea dataset, by comparing the proposed algorithm with its rigid variation, and two mutual information-based algorithms. The results indicate that the LSQ freeform version has the best performance for the nonrigid simulation and real datasets, with less than 0.15 pixel error given 1 pixel nonrigid distortion in the hyperspectral domain.

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Cite

Text

Zhou et al. "Nonrigid Registration of Hyperspectral and Color Images with Vastly Different Spatial and Spectral Resolutions for Spectral Unmixing and Pansharpening." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.201

Markdown

[Zhou et al. "Nonrigid Registration of Hyperspectral and Color Images with Vastly Different Spatial and Spectral Resolutions for Spectral Unmixing and Pansharpening." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/zhou2017cvprw-nonrigid/) doi:10.1109/CVPRW.2017.201

BibTeX

@inproceedings{zhou2017cvprw-nonrigid,
  title     = {{Nonrigid Registration of Hyperspectral and Color Images with Vastly Different Spatial and Spectral Resolutions for Spectral Unmixing and Pansharpening}},
  author    = {Zhou, Yuan and Rangarajan, Anand and Gader, Paul D.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2017},
  pages     = {1571-1579},
  doi       = {10.1109/CVPRW.2017.201},
  url       = {https://mlanthology.org/cvprw/2017/zhou2017cvprw-nonrigid/}
}