2018
DOI: 10.1109/jstars.2018.2809781
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Cascaded Random Forest for Hyperspectral Image Classification

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Cited by 93 publications
(45 citation statements)
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“…In the classification, the forest chooses the class having the most votes (over all the trees in the forest). In RF classification a split is determined by searching across a random subset of variables at each node [36,37].…”
Section: Random Forest Classifiermentioning
confidence: 99%
“…In the classification, the forest chooses the class having the most votes (over all the trees in the forest). In RF classification a split is determined by searching across a random subset of variables at each node [36,37].…”
Section: Random Forest Classifiermentioning
confidence: 99%
“…Random forest has many advantages, including high speed, strong parallelism, noise robustness, and an inherently multi-class nature. Due to these features, it is widely used in remote sensing image analysis [9][10][11][17][18][19][20][21][22][23][24]. However, RF suffers from the same problem as other popular classification methods: it requires many labeled samples to leverage its full potential.…”
Section: Semi-supervised Random Forestmentioning
confidence: 99%
“…Many of these applications are based on hyperspectral image (HSI) classification at the pixel level. In the past few years, various supervised classification methods, e.g., support vector machines (SVMs) [5,6], neural networks [7,8], and random forests (RFs) [9][10][11] have been successfully used for HSI classification. However, supervised methods often require many informative samples with labels to train high-performing classifiers.…”
Section: Introductionmentioning
confidence: 99%
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“…The results suggest that the method may be of assistance to image processing applications which rely on a transformation for data reduction as a first step of further processing. For examples of relevant applications we refer to [24][25][26][27][28].…”
Section: Introductionmentioning
confidence: 99%