Collaborative total variation for hyperspectral pansharpening

Variational methods are widely used in image processing for problems ranging from denoising to data fusion. In this paper we focus on a recent regularization method, called Collaborative Total Variation, applied to the hyperspectral pansharpening, which deals with the fusion of low resolution hyperspectral and high resolution panchromatic images. The effectiveness of this novel approach is evaluated for different Collaborative Norms and the assessment is performed on the Pavia University dataset.

[1]  L. Wald,et al.  Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images , 1997 .

[2]  Naoto Yokoya,et al.  Hyperspectral Pansharpening: A Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[3]  Jocelyn Chanussot,et al.  A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  W. Marsden I and J , 2012 .

[6]  José M. Bioucas-Dias Hyperspectral Remote Sensing Data Analysis , 2015, GISTAM.

[7]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[8]  Gang Yang,et al.  Missing Information Reconstruction of Remote Sensing Data: A Technical Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[9]  José M. Bioucas-Dias,et al.  Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Jocelyn Chanussot,et al.  A Critical Comparison Among Pansharpening Algorithms , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Jocelyn Chanussot,et al.  Hyperspectral pansharpening using convex optimization and collaborative total variation regularization , 2016, 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[12]  Michael Möller,et al.  Collaborative Total Variation: A General Framework for Vectorial TV Models , 2015, SIAM J. Imaging Sci..

[13]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..