Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest

Shadman Roodposhti, Majid; Aryal, Jagannath; Lucieer, Arko; Bryan, Brett A.

doi:10.3390/e21010078

Cited by 69Remote-Sensing Image ClassificationSpectroscopy and Chemometric AnalysesAdvanced Neural Network Applications

Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest

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Shadman Roodposhti, Majid, Aryal, Jagannath, Lucieer, Arko, Bryan, Brett A.·2019·Entropy

DOI 10.3390/e21010078PMC PMC7514187

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Abstract

Uncertainty assessment techniques have been extensively applied as an estimate of accuracy to compensate for weaknesses with traditional approaches. Traditional approaches to mapping accuracy assessment have been based on a confusion matrix, and hence are not only dependent on the availability of test data but also incapable of capturing the spatial variation in classification error. Here, we apply and compare two uncertainty assessment techniques that do not rely on test data availability and enable the spatial characterisation of classification accuracy before the validation phase, promoting the assessment of error propagation within the classified imagery products. We compared the performance of emerging deep neural network (DNN) with the popular random forest (RF) technique. Uncertainty assessment was implemented by calculating the Shannon entropy of class probabilities predicted by DNN and RF for every pixel. The classification uncertainties of DNN and RF were quantified for two different hyperspectral image datasets—Salinas and Indian Pines. We then compared the uncertainty against the classification accuracy of the techniques represented by a modified root mean square error (RMSE). The results indicate that considering modified RMSE values for various sample sizes of both datasets, the derived entropy based on the DNN algorithm is a better estimate of classification accuracy and hence provides a superior uncertainty estimate at the pixel level.

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DL-based methods have accurate detection and classification only if the training and test datasets are from the same or similar environment.
“DL-based methods have the advantage of accurate detection and classification, but only if the training and test datasets are from the same or similar environment [42] such that they are not affected by contextual and spatial uncertainties [43].”
From Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network (2022) · cites this paper as [43]
The Methods (§2.1) say training and test samples were randomly drawn from each dataset, and the Results (§3.2) report that the deep neural network classified more accurately than random forest on Indian Pines. The paper does not compare performance across different training and test environments, so it does not establish that accurate classification occurs only when those environments are similar. (LLM, conf=0.84)
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R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 49,574 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
A mathematical theory of communication
Hockett, C. F., Shannon, C., & Weaver, W. (1948) · MOCO · doi:10.1145/584091.584093 · Cited by 30,529
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · see citing works
Bagging predictors
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit
Cohen, J. (1968) · Psychological Bulletin · doi:10.1037/h0026256 · Cited by 8,736 · see citing works
A review of assessing the accuracy of classifications of remotely sensed data
Congalton, R. G. (1991) · Remote Sensing of Environment · doi:10.1016/0034-4257(91)90048-b · Cited by 7,798 · see citing works
Random forest in remote sensing: A review of applications and future directions
Belgiu, M., & Drăguţ, L. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.01.011 · Cited by 7,228 · see citing works
Status of land cover classification accuracy assessment
Foody, G. M. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00295-4 · Cited by 4,655 · see citing works
A survey of image classification methods and techniques for improving classification performance
Lu, D., & Weng, Q. (2007) · International Journal of Remote Sensing · doi:10.1080/01431160600746456 · Cited by 3,424 · see citing works
Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679 · see citing works
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Zhang, L., Zhang, L., & Du, B. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2540798 · Cited by 2,229 · see citing works
Selecting and interpreting measures of thematic classification accuracy
Stehman, S. V. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00083-7 · Cited by 1,745 · see citing works
Remote Sensing Digital Image Analysis
Richards, J. A., & Jia, X. (1999) · Springer eBooks · doi:10.1007/978-3-662-03978-6 · Cited by 1,538 · see citing works
Remote sensing imagery in vegetation mapping: a review
Xie, Y., Sha, Z., & Yu, M. (2008) · Journal of Plant Ecology · doi:10.1093/jpe/rtm005 · Cited by 1,414 · see citing works
Investigation of the random forest framework for classification of hyperspectral data
Ham, J., Chen, Y., Crawford, M. M., & Ghosh, J. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.842481 · Cited by 1,289 · see citing works
Assessing the Accuracy of Remotely Sensed Data
Congalton, R. G., & Green, K. (2008) · CRC Press eBooks · doi:10.1201/9781420055139 · Cited by 1,278 · see citing works
Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
Chan, J. C.-W., & Paelinckx, D. (2008) · Remote Sensing of Environment · doi:10.1016/j.rse.2008.02.011 · Cited by 861 · see citing works
Feature Selection for Classification of Hyperspectral Data by SVM
Pal, M., & Foody, G. M. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2039484 · Cited by 818 · see citing works
Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing
Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., & Huang, Z. (2005) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2004.12.005 · Cited by 723 · see citing works
Convolutional neural networks for hyperspectral image classification
Yu, S., Jia, S., & Xu, C. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.09.010 · Cited by 709 · see citing works
Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)
Lawrence, R. L., Wood, S. D., & Sheley, R. L. (2005) · Remote Sensing of Environment · doi:10.1016/j.rse.2005.10.014 · Cited by 706 · see citing works
An empirical evaluation of supervised learning in high dimensions
Caruana, R., Karampatziakis, N., & Yessenalina, A. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390169 · Cited by 531 · see citing works
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery
Li, C., Wang, J., Wang, L., Hu, L., & Gong, P. (2014) · Remote Sensing · doi:10.3390/rs6020964 · Cited by 414 · see citing works
The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM
Foody, G. M., & Mathur, A. (2006) · Remote Sensing of Environment · doi:10.1016/j.rse.2006.04.001 · Cited by 411 · see citing works
Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification
Foody, G. M., Campbell, N. A., Trodd, N., & Wood, T. (1992) · Research Repository (Kingston University London) · Cited by 344 · see citing works
Uncertainty in the spatial prediction of soil texture
Ließ, M., Glaser, B., & Huwe, B. (2011) · Geoderma · doi:10.1016/j.geoderma.2011.10.010 · Cited by 299 · see citing works
Active Deep Learning for Classification of Hyperspectral Images
Liu, P., Zhang, H., & Eom, K. B. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2598859 · Cited by 290 · see citing works
Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment
Naidoo, L., Cho, M. A., Mathieu, R., & Asner, G. P. (2012) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2012.03.005 · Cited by 287 · see citing works
MugNet: Deep learning for hyperspectral image classification using limited samples
Pan, B., Shi, Z., & Xu, X. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.11.003 · Cited by 255 · see citing works
Spatial analysis of remote sensing image classification accuracy
Comber, A., FISHER, P. F., Brunsdon, C., & Khmag, A. E. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.09.005 · Cited by 237 · see citing works
A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches
Ye, S., Pontius, R. G., & Rakshit, R. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.002 · Cited by 199 · see citing works
Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites
Heydari, S. S., & Mountrakis, G. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.09.035 · Cited by 184 · see citing works
Approximating Prediction Uncertainty for Random Forest Regression Models
Coulston, J. W., Blinn, C. E., Thomas, V. A., & Wynne, R. H. (2016) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.82.3.189 · Cited by 168 · see citing works
Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition
Li, L., Zhao, Y., Jiang, D., Zhang, Y., Wang, F., Gonzalez, I., Valentin, E., & Sahli, H. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction · doi:10.1109/acii.2013.58 · Cited by 163 · see citing works
Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery
Luo, F., Huang, H. B., Duan, Y., Liu, J., & Liao, Y. (2017) · Remote Sensing · doi:10.3390/rs9080790 · Cited by 151 · see citing works
Big data integration shows Australian bush-fire frequency is increasing significantly
Dutta, R., Das, A., & Aryal, J. (2016) · Royal Society Open Science · doi:10.1098/rsos.150241 · Cited by 139 · see citing works
Self-citation
Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification
Loosvelt, L., Peters, J., Skriver, H., Lievens, H., Coillie, F. M. B. V., Bernard De Baets, C., & Verhoest, N. E. C. (2012) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2012.05.011 · Cited by 122 · see citing works
Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods
McIver, D. K., & Friedl, M. A. (2001) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.951086 · Cited by 108 · see citing works
Drought sensitivity mapping using two one-class support vector machine algorithms
Roodposhti, M. S., Safarrad, T., & Shahabi, H. (2017) · Atmospheric Research · doi:10.1016/j.atmosres.2017.04.017 · Cited by 104 · see citing works
Measures of spatio-temporal accuracy for time series land cover data
Tsutsumida, N., & Comber, A. (2015) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2015.04.018 · Cited by 95 · see citing works
Evaluating the uncertainty of area estimates derived from fuzzy land-cover classification.
Canters, F. (1997) · Cited by 85 · see citing works
Mapping per-pixel predicted accuracy of classified remote sensing images
Khatami, R., Mountrakis, G., & Stehman, S. V. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.01.025 · Cited by 84 · see citing works
Analyzing Training Information From Random Forests for Improved Image Segmentation
Mahapatra, D. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2305073 · Cited by 77 · see citing works
Measurement of uncertainty by the entropy: application to the classification of MSS data
Dehghan, H., & Ghassemian, H. (2006) · International Journal of Remote Sensing · doi:10.1080/01431160600647225 · Cited by 62 · see citing works
Alternative representations of in‐stream habitat: classification using remote sensing, hydraulic modeling, and fuzzy logic
Legleiter, C. J., & Goodchild, M. F. (2005) · International Journal of Geographical Information Systems · doi:10.1080/13658810412331280220 · Cited by 57 · see citing works
DenseNet-Based Depth-Width Double Reinforced Deep Learning Neural Network for High-Resolution Remote Sensing Image Per-Pixel Classification
Tao, Y., Xu, M., Lu, Z., & Zhong, Y. (2018) · Remote Sensing · doi:10.3390/rs10050779 · Cited by 55 · see citing works
Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs
Giacco, F., Thiel, C., Pugliese, L., Scarpetta, S., & Marinaro, M. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2047863 · Cited by 53 · see citing works
Urban Remote Sensing
Shan, J., Weng, Q., Ehlers, M., Quattrochi, D. A., Zhou, G., Stilla, U., Song, C., Myint, S. W., Herold, M., Liu, W., et al. (2018) · CRC Press eBooks · doi:10.1201/9781315166612 · Cited by 52 · see citing works
Nonparametric feature extraction for classification of hyperspectral images with limited training samples
Kianisarkaleh, A., & Ghassemian, H. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.05.009 · Cited by 50 · see citing works
Estimating per‐pixel thematic uncertainty in remote sensing classifications
Brown, K. M., Foody, G. M., & Atkinson, P. M. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160802290568 · Cited by 36 · see citing works
A spectral-spatial kernel-based method for hyperspectral imagery classification
Li, L., Ge, H., & Gao, J. (2016) · Advances in Space Research · doi:10.1016/j.asr.2016.11.006 · Cited by 33 · see citing works
Deep Cube-Pair Network for Hyperspectral Imagery Classification
Wei, W., Zhang, J., Lei Zhang, A., Tian, C., & Zhang, Y. (2018) · Remote Sensing · doi:10.3390/rs10050783 · Cited by 33 · see citing works
Discriminative Feature Metric Learning in the Affinity Propagation Model for Band Selection in Hyperspectral Images
Yang, C., Tan, Y., Bruzzone, L., Lu, L., & Guan, R. (2017) · Remote Sensing · doi:10.3390/rs9080782 · Cited by 31 · see citing works
Factors Affecting Spatial Variation of Classification Uncertainty in an Image Object-based Vegetation Mapping
Yu, Q., Gong, P., Tian, Y. Q., Pu, R., & Yang, J. (2008) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.74.8.1007 · Cited by 31 · see citing works
Random forests of binary hierarchical classifiers for analysis of hyperspectral data
Crawford, M. M., Ham, J., Chen, Y., & Ghosh, J. (2004) · IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data · doi:10.1109/warsd.2003.1295213 · Cited by 29 · see citing works
Performance evaluation of hyperspectral classification algorithms on AVIRIS mineral data
Adep, R. N., Vijayan, A. P., Shetty, A., & Ramesh, H. (2016) · Perspectives in Science · doi:10.1016/j.pisc.2016.06.070 · Cited by 26 · see citing works
The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping
Abdel-Rahman, E. M., Makori, D. M., Landmann, T., Piiroinen, R., Gasim, S. K., Pellikka, P., & Raina, S. K. (2015) · Remote Sensing · doi:10.3390/rs71013298 · Cited by 25 · see citing works
Unsupervised classification based on fuzzyc-means with uncertainty analysis
Wang, Q., & Shi, W. (2013) · Remote Sensing Letters · doi:10.1080/2150704x.2013.832842 · Cited by 22 · see citing works
Modeling the Probability of Misclassification in a Map of Land Cover Change
Burnicki, A. C. (2011) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.77.1.39 · Cited by 19 · see citing works
Assessing hydrologic prediction uncertainty resulting from soft land cover classification
Loosvelt, L., Bernard De Baets, C., Pauwels, V. R. N., & Verhoest, N. E. C. (2014) · Journal of Hydrology · doi:10.1016/j.jhydrol.2014.05.049 · Cited by 9 · see citing works
A simple measure of confidence for fuzzy land-cover classification from remote-sensing data
Prasad, M. S. G., & Arora, M. K. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.979303 · Cited by 5 · see citing works

Flagged first, then by citations

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Research works printed but not linked: 3

[42]Acquarelli J. Marchiori E. Buydens L. Tran T. van Laarhoven T. Convolutional neural networks and data augmentation for spectral-spatial classification of hyperspectral images arXiv 2017 1711.05512
[64]Guo C. Pleiss G. Sun Y. Weinberger K.Q. On calibration of modern neural networks arXiv 2017 1706.04599
[65]Zhu D. Yao H. Jiang B. Yu P. Negative log likelihood ratio loss for deep neural network classification arXiv 2018 1804.10690

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Lu, L., Lei, J. S., Qin, X., Zhang, J., Wang, S., & Wang, C. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101423 · Cited by 3 · see citing works
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Eslami, P., & Song, H. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592478 · Cited by 1 · see citing works
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グーメイ, エ., & Goumehei, E. (2019) · Institutional Repositories DataBase (IRDB)
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de Figueiredo, R. M., de Arruda, D. C., Bender, E., Müller, L. E., Souza, G. E., Salem, M. C., Gonçalves, S. R. D. S., Nogueira, B. D. S., Gallo, C. B. A. B. B., & Gallo, B. B. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1015985
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Essel-Yorke, K. A., Ansah-Narh, T., Albers, P. P., Tandoh, J. B., & Nyarko, B. K. (2026) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2026.102240

Description and evaluation approach for uncertainty of RS images classification
Lan, Z., Liu, Y., Tang, X., & Liu, Y. (2009) · Geo-spatial Information Science · doi:10.1007/s11806-009-0162-8 · Cited by 1 · see citing works
Youth Forum
Huan Li, A., Hongxia Luo, A., Ziyi Zhu, B., & Guangpeng Liu, A. (2011)
A general method for assessing the uncertainty in classified remotely sensed data at pixel scale
Bo, Y., & Wang, J. (2008) · Cited by 1 · see citing works
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He, X. J., Chen, Y., & Huang, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3176913 · Cited by 15 · see citing works
Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification
Loosvelt, L., Peters, J., Skriver, H., Lievens, H., Coillie, F. M. B. V., Bernard De Baets, C., & Verhoest, N. E. C. (2012) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2012.05.011 · Cited by 122 · see citing works

Authors: Shadman Roodposhti, M., Aryal, J., Lucieer, A., & Bryan, B. A.
Affiliations: Discipline of Geography and Spatial Sciences, School of Technology, Environments and Design, University of Tasmania, Hobart 7018, Australia
University of Tasmania (AU)
Deakin University (AU)
ORCID: Aryal, Jagannath
Lucieer, Arko
Bryan, Brett A.
Venue: Entropy
Publisher: MDPI AG
Published: 2019-01-16
Volume: 21
Issue: 1
Pages: 78
ISSN: 1099-4300 (ISSN-L)
DOI: 10.3390/e21010078
PMC: PMC7514187
OpenAlex: W2910306855
Cited by: 65
References: 69
Citations by year: 2026: 6 · 2025: 6 · 2024: 2 · 2023: 8 · 2022: 25 · 2021: 8 · 2020: 6 · 2019: 4
FWCI: 7.21
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Entropy (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
UTAS Research Repository (acceptedVersion)
Minerva Access (University of Melbourne) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Own your potential (DEAKIN) (submittedVersion)
Figshare (submittedVersion, cc-by)
Swinburne figshare (Swinburne University of Technology) (submittedVersion, other-oa)
Funders: Commonwealth Scientific and Industrial Research Organisation (RT109121)
Topics: Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: uncertainty analysis, hyperspectral image classification, Shannon entropy, deep neural network, random forest, classification accuracy, error propagation, Salinas dataset, Indian Pines dataset

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Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest

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