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Image fusion and reconstruction of compressed data: A joint approach

Abstract : In the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter. In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources. The burden of data downlink is hence significantly reduced at the expense of a more laborious analysis done at the ground segment to estimate the missing information. The reconstruction algorithm estimates the target sharpened image directly instead of decompressing the original sources beforehand; a viable and practical novel solution is also introduced to show the effectiveness of the approach.
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Submitted on : Monday, July 30, 2018 - 12:18:52 PM
Last modification on : Wednesday, November 3, 2021 - 7:49:53 AM
Long-term archiving on: : Wednesday, October 31, 2018 - 1:00:10 PM


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  • HAL Id : hal-01851515, version 1



Daniele Picone, Laurent Condat, Florian Cotte, Mauro Dalla Mura. Image fusion and reconstruction of compressed data: A joint approach. ICIP 2018 - 25th IEEE International Conference on Image Processing, Oct 2018, Athènes, Greece. ⟨hal-01851515⟩



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