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Conference Papers Year : 2016

From local to global unmixing of hyperspectral images to reveal spectral variability

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Abstract

The linear mixing model is widely assumed when unmixing hyperspectral images, but it cannot account for endmembers spectral variability. Thus, several workarounds have arisen in the hyperspectral unmixing literature, such as the extended linear mixing model (ELMM), which authorizes endmembers to vary pixelwise according to scaling factors, or local spectral unmixing (LSU) where the unmixing process is conducted locally within the image. In the latter case however, results are difficult to interpret at the whole image scale. In this work, we propose to analyze the local results of LSU within the ELMM framework, and show that it not only allows to reconstruct global endmembers and fractional abundances from the local ones, but it also gives access to the scaling factors advocated by the ELMM. Results obtained on a real hyperspectral image confirm the soundness of the proposed methodology.
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Dates and versions

hal-01356156 , version 1 (25-08-2016)

Identifiers

  • HAL Id : hal-01356156 , version 1

Cite

Guillaume Tochon, Lucas Drumetz, Miguel Angel Veganzones, Mauro Dalla Mura, Jocelyn Chanussot. From local to global unmixing of hyperspectral images to reveal spectral variability. WHISPERS 2016 - 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Aug 2016, Los Angeles, CA, United States. ⟨hal-01356156⟩
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