ANALYSIS OF OPTIMAL INPUT COMBINATION IN RANDOM FOREST ALGORITHM FOR LAND COVER CLASSIFICATION USING SENTINEL 1 AND SENTINEL 2 SATELLITE IMAGES
Land cover classification has an important role in monitoring the condition of the earth's surface. Conventional field survey methods are not efficient and accurate in collecting land cover information, but remote sensing technology allows monitoring of land cover. One method that has a leve...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/76586 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Land cover classification has an important role in monitoring the condition of the
earth's surface. Conventional field survey methods are not efficient and accurate
in collecting land cover information, but remote sensing technology allows
monitoring of land cover. One method that has a level of effectiveness in land
cover classification with remote sensing data is the random forest method, the
random forest method approach can handle the complexity of many features. The
combination of random forest model determination can be influenced by the
number of features used, the number of decision points, and the ratio of training
and test data. The optimal combination of parameters will provide an
implementation that has a better level of confidence. In this study, remote sensing
imagery used sentinel images 1 and 2. The study found that the model with 11
parameters, 100 decision trees and 70:30 test training ratio achieved a test
accuracy rate of 0.764 and a computational time of 0.3 seconds. The applied
model was implemented in the research area which produced a kappa coefficient
of 0.45 with a sufficient level of confidence (moderate).
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