Image classification is one of the most widely used techniques on
remote sensing data. Whereas the actual supervised classification
of satellite image is a highly automated process, assembling the
training data needed for such classification is anything but
automatic. Our research has focused on developing a method to
extract the training samples for some classes automatically from
the data itself by analyzing the discrete fourier transform of the
temporal signatures of every class. The multi- temporal data allows
us to characterize objects based on their dynamic processes rather
than static properties like color, shape, etc and this is being
successfully demonstrated in this research. Other utilities of the
time series data like derivation of a season calendar, mapping of
cropping practices and finding single and double cropping regions
have been demonstrated. The solution to the problem of finding
single and multi crop regions enables us to implement policy
decisions like Special Economic Zone (SEZ) Act, 2002 which
prohibits the use of multi cropping lands for setting up special
economic zones.
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