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Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest
Citation
Roodposhti, MS and Aryal, J and Lucieer, A and Bryan, BA, Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest, Entropy, 21, (1) Article 78. ISSN 1099-4300 (2019) [Refereed Article]
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Copyright Statement
Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
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.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | uncertainty assessment, deep neural network, random forest, Shannon entropy |
| Research Division: | Engineering |
| Research Group: | Geomatic engineering |
| Research Field: | Photogrammetry and remote sensing |
| Objective Division: | Expanding Knowledge |
| Objective Group: | Expanding knowledge |
| Objective Field: | Expanding knowledge in the environmental sciences |
| UTAS Author: | Roodposhti, MS (Mr Majid Roodposhti) |
| UTAS Author: | Aryal, J (Dr Jagannath Aryal) |
| UTAS Author: | Lucieer, A (Professor Arko Lucieer) |
| ID Code: | 131340 |
| Year Published: | 2019 |
| Web of Science® Times Cited: | 22 |
| Deposited By: | Geography and Spatial Science |
| Deposited On: | 2019-03-13 |
| Last Modified: | 2020-05-19 |
| Downloads: | 36 View Download Statistics |
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