% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Schmitter:845666,
      author       = {Schmitter, P. and Steinrücken, J. and Römer, C. and
                      Ballvora, A. and Léon, J. and Rascher, U. and Plümer, L.},
      title        = {{U}nsupervised domain adaptation for early detection of
                      drought stress in hyperspectral images},
      journal      = {ISPRS journal of photogrammetry and remote sensing},
      volume       = {131},
      issn         = {0924-2716},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2018-02879},
      pages        = {65 - 76},
      year         = {2017},
      abstract     = {Hyperspectral images can be used to uncover physiological
                      processes in plants if interpreted properly. Machine
                      Learning methods such as Support Vector Machines (SVM) and
                      Random Forests have been applied to estimate development of
                      biomass and detect and predict plant diseases and drought
                      stress. One basic requirement of machine learning implies,
                      that training and testing is done in the same domain and the
                      same distribution. Different genotypes, environmental
                      conditions, illumination and sensors violate this
                      requirement in most practical circumstances. Here, we
                      present an approach, which enables the detection of
                      physiological processes by transferring the prior knowledge
                      within an existing model into a related target domain, where
                      no label information is available. We propose a two-step
                      transformation of the target features, which enables a
                      direct application of an existing model. The transformation
                      is evaluated by an objective function including additional
                      prior knowledge about classification and physiological
                      processes in plants. We have applied the approach to three
                      sets of hyperspectral images, which were acquired with
                      different plant species in different environments observed
                      with different sensors. It is shown, that a classification
                      model, derived on one of the sets, delivers satisfying
                      classification results on the transformed features of the
                      other data sets. Furthermore, in all cases early
                      non-invasive detection of drought stress was possible.},
      cin          = {IBG-2},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582)},
      pid          = {G:(DE-HGF)POF3-582},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000411775100006},
      doi          = {10.1016/j.isprsjprs.2017.07.003},
      url          = {https://juser.fz-juelich.de/record/845666},
}