Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 753-760, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-753-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 753-760, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-753-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

AN IMPROVED VARIATIONAL METHOD FOR HYPERSPECTRAL IMAGE PANSHARPENING WITH THE CONSTRAINT OF SPECTRAL DIFFERENCE MINIMIZATION

Z. Huang1, Q. Chen1,2, Y. Shen1, Q. Chen1, and X. Liu1 Z. Huang et al.
  • 1Faculty of Information Engineering, China University of Geoscience, Wuhan, China
  • 2Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan

Keywords: Image fusion, Pansharpening, Hyperspectral image, Variational model, Spectral fidelity, Spectral difference minimization

Abstract. Variational pansharpening can enhance the spatial resolution of a hyperspectral (HS) image using a high-resolution panchromatic (PAN) image. However, this technology may lead to spectral distortion that obviously affect the accuracy of data analysis. In this article, we propose an improved variational method for HS image pansharpening with the constraint of spectral difference minimization. We extend the energy function of the classic variational pansharpening method by adding a new spectral fidelity term. This fidelity term is designed following the definition of spectral angle mapper, which means that for every pixel, the spectral difference value of any two bands in the HS image is in equal proportion to that of the two corresponding bands in the pansharpened image. Gradient descent method is adopted to find the optimal solution of the modified energy function, and the pansharpened image can be reconstructed. Experimental results demonstrate that the constraint of spectral difference minimization is able to preserve the original spectral information well in HS images, and reduce the spectral distortion effectively. Compared to original variational method, our method performs better in both visual and quantitative evaluation, and achieves a good trade-off between spatial and spectral information.