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@INPROCEEDINGS{Sharma:1031803,
      author       = {Sharma, Surbhi and Sedona, Rocco and Riedel, Morris and
                      Cavallaro, Gabriele and Paris, Claudia},
      title        = {{E}nhancing {L}and {C}over {M}apping: {A} {N}ovel
                      {A}utomatic {A}pproach {T}o {I}mprove {M}ixed {S}pectral
                      {P}ixel {C}lassification},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-05823},
      pages        = {1017-1020},
      year         = {2024},
      abstract     = {The increasing availability of high-resolution, open-access
                      satellite data facilitates the production of global land
                      cover (LC) maps, an essential source of information for
                      managing and monitoring natural and human-induced processes.
                      However, the accuracy of the obtained LC maps can be
                      affected by the discrepancy between the spatial resolution
                      of the satellite images and the extent of the LC present in
                      the scene. Indeed, several pixels may be misclassified
                      because of their mixed spectral signatures, i.e., more than
                      two LC classes are present in the pixel. To solve this
                      problem, this paper proposes an approach that explores the
                      possibility of using simple but effective unmixing
                      techniques to enhance the classification accuracy of the
                      mixed spectral pixels. The results showed that several
                      pixels, including buildings and grassland LC, are typically
                      classified as cropland. By unmixing their spectral content,
                      it is possible to extract the most prevalent class within
                      the area of each pixel to update the classification map,
                      thus sharply increasing the map accuracy. These promising
                      preliminary results indicate the potential for broader
                      applicability and efficiency in global LC mapping.},
      month         = {Jul},
      date          = {2024-07-07},
      organization  = {IGARSS 2024 - 2024 IEEE International
                       Geoscience and Remote Sensing
                       Symposium, Athens (Greece), 7 Jul 2024
                       - 12 Jul 2024},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / ADMIRE - Adaptive
                      multi-tier intelligent data manager for Exascale (956748) /
                      Verbundprojekt: ADMIRE - Adaptives Datenmanagement für das
                      Exascale-Computing (16HPC008) / EUROCC-2 (DEA02266)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)956748 / G:(BMBF)16HPC008
                      / G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001316158501095},
      doi          = {10.1109/IGARSS53475.2024.10641976},
      url          = {https://juser.fz-juelich.de/record/1031803},
}