Keywords
OverfittingRandom forestClassifier (UML)Computer scienceOutlierArtificial intelligencePattern recognition (psychology)Curse of dimensionalityMachine learningDecision treeRemote sensingData miningArtificial neural networkGeography
Affiliated Institutions
Related Publications
The random subspace method for constructing decision forests
Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is ...
Implementation of machine-learning classification in remote sensing: an applied review
Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle dat...
Combining randomization and discrimination for fine-grained image categorization
In this paper, we study the problem of fine-grained image categorization. The goal of our method is to explore fine image statistics and identify the discriminative image patche...
Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging
We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussi...
Publication Info
- Year
- 2016
- Type
- review
- Volume
- 114
- Pages
- 24-31
- Citations
- 6030
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
6030
OpenAlex
Cite This
Mariana Belgiu,
Lucian Drăguţ
(2016).
Random forest in remote sensing: A review of applications and future directions.
ISPRS Journal of Photogrammetry and Remote Sensing
, 114
, 24-31.
https://doi.org/10.1016/j.isprsjprs.2016.01.011
Identifiers
- DOI
- 10.1016/j.isprsjprs.2016.01.011