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@ARTICLE{Scharr:897480,
      author       = {Scharr, Hanno and Rademske, Patrick and Alonso, Luis and
                      Cogliati, Sergio and Rascher, Uwe},
      title        = {{S}patio-spectral deconvolution for high resolution
                      spectral imaging with an application to the estimation of
                      sun-induced fluorescence},
      journal      = {Remote sensing of environment},
      volume       = {267},
      issn         = {0034-4257},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2021-03813},
      pages        = {112718 -},
      year         = {2021},
      abstract     = {We propose a signal deconvolution procedure for imaging
                      spectrometer data, where a measured point spread function
                      (PSF) is deconvolved itself before being used for
                      deconvolution of the signal. We evaluate the effectiveness
                      of our procedure for improvement of the spatio-spectral
                      signal, as well as our target application, i.e. estimation
                      of sun-induced fluorescence (SIF). Imaging spectrometers are
                      well established instruments for remote sensing. When used
                      for scientific purposes these instruments are usually
                      calibrated on a regular basis. In our case the point spread
                      function of the optics is measured in an elaborate procedure
                      with a tunable monochromator point light source. PSFs are
                      measured at different pixel positions of the imaging sensor,
                      i.e. at different spatio-spectral locations, and averaged in
                      order to get an as accurate PSF as possible. We investigate
                      error sources in this calibration process by simulating the
                      procedure in silico. Averaging as well as the spectral and
                      spatial width of the point source introduce some smoothness
                      in the measured PSF. We propose corrective measures, i.e.
                      deconvolution of the PSF itself and median instead of mean
                      averaging, leading to a set of sharper PSFs. We test the
                      performance of these PSFs in deconvolving simulated as well
                      as real hyperspectral images. For deconvolution we test a
                      set of well-known, off the shelf deconvolution algorithms.
                      Quantitatively in terms of PSNR (Peak Signal to Noise Ratio)
                      a combination of Wiener filtering and sharpened PSFs yields
                      strongest improvements, while using Wiener filtering with
                      non-sharpened PSFs even deteriorates the signal. Comparing
                      deconvolution results of the simulated data with results of
                      real data reveals, that visually very similar effects can be
                      observed. This well supports the assumption, that our
                      findings are also valid for real spatio-spectral data.
                      Surprisingly, the choice of PSF, sharpened or not, is of
                      little effect for SIF estimation with the iFLD algorithm in
                      the O2A band. Quantitatively we find that deconvolution
                      reduces the overall error of SIF by a factor of 3.8, when
                      using Wiener filtering instead of the currently used 1
                      iteration of vanCittert's method. For SIF estimation in the
                      O2B band we observe a totally different behavior, where all
                      deconvolution methods yield unreliable results with mostly
                      well above $200\%$ relative error and high standard
                      deviations. In the discussion we can only speculate on
                      possible reasons for this unreliability. As conclusion we
                      therefore propose to use the O2A band for SIF estimation
                      together with classic Wiener filtering for deconvolution of
                      spatio-spectral data.},
      cin          = {IAS-8 / IBG-2},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IAS-8-20210421 / I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2171},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000708567400002},
      doi          = {10.1016/j.rse.2021.112718},
      url          = {https://juser.fz-juelich.de/record/897480},
}