Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota.
Jennifer Marie CorcoranJoseph F. KnightAlisa L. GallantPublished in: Remote. Sens. (2013)
Keyphrases
- multi source
- random forest
- multiple sources
- data sources
- decision trees
- classification accuracy
- data sets
- data collection
- data analysis
- knowledge discovery
- database
- remotely sensed
- heterogeneous data
- satellite images
- information fusion
- data fusion
- image data
- data points
- feature vectors
- databases
- multi class
- training samples
- prediction accuracy
- infrared
- remote sensing
- change detection
- training data
- database systems
- image processing
- computer vision
- neural network