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
Jan 20, 2018
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]