Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest

Roodposhti, Majid Shadman, Aryal, Jagannath, Lucieer, Arko and Bryan, Brett A. 2019, Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest, Entropy, vol. 21, no. 1, pp. 1-15, doi: 10.3390/e21010078.

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Title Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest
Author(s) Roodposhti, Majid Shadman
Aryal, Jagannath
Lucieer, Arko
Bryan, Brett A.ORCID iD for Bryan, Brett A. orcid.org/0000-0003-4834-5641
Journal name Entropy
Volume number 21
Issue number 1
Start page 1
End page 15
Total pages 15
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2019-01-01
ISSN 1099-4300
Keyword(s) uncertainty assessment
deep neural network
random forest
Shannon entropy
Science & Technology
Physical Sciences
Physics, Multidisciplinary
Physics
LAND-COVER CLASSIFICATION
PER-PIXEL
PREDICTION UNCERTAINTY
ACCURACY
TREE
REPRESENTATIONS
CONFIDENCE
SELECTION
Language eng
DOI 10.3390/e21010078
Field of Research 01 Mathematical Sciences
02 Physical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Persistent URL http://hdl.handle.net/10536/DRO/DU:30118662

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