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A robust rule-based ensemble framework using mean-shift segmentation for hyperspectral image classification

Citation

Roodposhti, MS and Lucieer, A and Anees, A and Bryan, BA, A robust rule-based ensemble framework using mean-shift segmentation for hyperspectral image classification, Remote Sensing, 11, (17) pp. 1-20. ISSN 2072-4292 (2019) [Refereed Article]


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Copyright Statement

Copyright 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

DOI: doi:10.3390/rs11172057

Abstract

This paper assesses the performance of DoTRules-a dictionary of trusted rules-as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.

Item Details

Item Type:Refereed Article
Keywords:image classification, ensemble, mean-shift, entropy, uncertainty map
Research Division:Environmental Sciences
Research Group:Ecological applications
Research Field:Ecological applications not elsewhere classified
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:Lucieer, A (Professor Arko Lucieer)
UTAS Author:Anees, A ( Adil Anees)
ID Code:152157
Year Published:2019
Web of Science® Times Cited:6
Deposited By:Engineering
Deposited On:2022-08-12
Last Modified:2022-09-05
Downloads:3 View Download Statistics

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