Kulkarni, Keerti
Mapping Forests Using an Imbalanced Dataset - Vol,103(6), Dec - New York Springer 2022 - 1987–1994p
Forests play a major role in maintaining the ecological stability of the region. In recent years, rampant tourism and other human activities have resulted in the decline of the area covered by forests. Many of the times, it becomes difficult to keep a track of the forest land lost, by regular land surveying. Machine learning classifiers applied to remotely sensed images can map the land cover of the region. The challenge in this experiment is that the classes are imbalanced, and hence the classifiers tend to be more biased toward the class which has a greater number of training samples. The novelty of the work is handling this imbalance at the training data level. This is done by using the area-proportionally sampled training samples for training the parameter tuned Random Forest Classifier. The results of this study revealed that, after the classifier is tuned, area-proportional allocation of training samples per class achieved the best classification results. The overall accuracy obtained is 90.5% and 94.6%, with a kappa of 0.85 and 0.92, respectively, for uniform sampling and area-proportional sampling methods.
Electrical Engineering
Mapping Forests Using an Imbalanced Dataset - Vol,103(6), Dec - New York Springer 2022 - 1987–1994p
Forests play a major role in maintaining the ecological stability of the region. In recent years, rampant tourism and other human activities have resulted in the decline of the area covered by forests. Many of the times, it becomes difficult to keep a track of the forest land lost, by regular land surveying. Machine learning classifiers applied to remotely sensed images can map the land cover of the region. The challenge in this experiment is that the classes are imbalanced, and hence the classifiers tend to be more biased toward the class which has a greater number of training samples. The novelty of the work is handling this imbalance at the training data level. This is done by using the area-proportionally sampled training samples for training the parameter tuned Random Forest Classifier. The results of this study revealed that, after the classifier is tuned, area-proportional allocation of training samples per class achieved the best classification results. The overall accuracy obtained is 90.5% and 94.6%, with a kappa of 0.85 and 0.92, respectively, for uniform sampling and area-proportional sampling methods.
Electrical Engineering