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November 20, 2014
Mapping croplands using Landsat data with generalized classifier over large
areas
Aparna Phalke and Prof. Mutlu Ozdogan
Nelson Institute for Environmental Studies University of Wisconsin - Madison
Updates on following
LDA model training and tuning
LDA results of sample footprints
Basic Definitions• Model tuning is the process in which one or more parameters of a device or model are adjusted upwards or downwards to achieve improved or specified results
• The aim of LDA model tuning is to calibrate the parameters of propagation models and improve the key performance indicators.
MethodologyR algorithm
• Divide training data in 75%-25% splits• LDA model trained on 75% data and tested on 25% data
• This procedure repeated 1000 times with random sets of train and test
• LDA model accuracy check with train and test within scene or within footprint.
ResultsGroup 1:
ResultsGroup 2:
ResultsGroup 3:
ResultsGroup 4:
ResultsGroup 5:
ResultsGroup 6:
ResultsLDA model accuracy at different
levels
LDA classified image sample result: Mea
nStd
Min Variance
Range
Counts
Slope Elevation
MaxInputs
LDA classified image results
turkey Group/zone Allmean 1.11E-03 6.10E-04 5.97E-04
sd -1.83E-03 -1.58E-03 -1.62E-03max -8.13E-05 -1.21E-04 -1.23E-04min -3.44E-05 -3.46E-04 -2.09E-04var 1.34E-07 1.82E-07 1.37E-07
range -7.63E-05 -5.60E-05 1.13E-05count 1.99E-02 -1.67E-02 1.02E-02slope 7.91E-02 9.21E-02 1.39E-01
elevation 2.82E-03 1.03E-03 1.88E-04
LDA coefficients of sample example
Own Within
group/zone All
LDA classified image sample results
Conclusion Model tuning helped us in understanding dynamics of whole process, which gives a more accurate picture of how the model is behaving.
Thank [email protected]