For future work, rules could be developed for remain-
ing agricultural land covers (wheat, rye, grasslands, etc.) of
the study area and the efficiency of rule-based classification
could be tested while working on several classes. Since maize
was one of the most difficult classes to identify in the study
area, it is expected that the classification methodology for
other crops will be successful with high accuracies. The rule
set development in this particular case was time taking but
it should be noted that maize had the most within-in class
variation. For simple cases,
NDVI
could be enough to separate
land covers as documented by Hernando
et al.,
(2012a). In
order for rule set to be transferable on multi-temporal and
other sensors data, DNs of orthophotos should be converted to
percentage ground reflectance before decision rules develop-
ment and classification. Moreover, new accuracy assessment
approaches for object-based image classification (Radoux
et
al.,
2010) such as Object Fate Analysis (
OFA
) matrix (Schöpfer
et al.,
2008; Hernando
et al.,
2012a) could be carried out to
further enhance the analysis.
Acknowledgments
The authors are grateful to German Federal Ministry for Edu-
cation and Research (
BMBF
) and Institute of Landscape and
Plant Ecology (320), University of Hohenheim for financing
the project by providing the data, software, and agricultural
assistance in making this research possible. We also thank
Trimble eCognition team for freely presenting a latest copy of
eCognition software.
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