{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:46:09Z","timestamp":1760240769566,"version":"build-2065373602"},"reference-count":81,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000943","name":"Commonwealth Scientific and Industrial Research Organisation","doi-asserted-by":"publisher","award":["RT109121"],"award-info":[{"award-number":["RT109121"]}],"id":[{"id":"10.13039\/501100000943","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper assesses the performance of DoTRules\u2014a dictionary of trusted rules\u2014as 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.<\/jats:p>","DOI":"10.3390\/rs11172057","type":"journal-article","created":{"date-parts":[[2019,9,2]],"date-time":"2019-09-02T03:16:12Z","timestamp":1567394172000},"page":"2057","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"11","author":[{"given":"Majid","family":"Shadman Roodposhti","sequence":"first","affiliation":[{"name":"Discipline of Geography and Spatial Sciences, School of Technology, Environments and Design, University of Tasmania, Churchill Ave, Hobart, TAS 7005, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9468-4516","authenticated-orcid":false,"given":"Arko","family":"Lucieer","sequence":"additional","affiliation":[{"name":"Discipline of Geography and Spatial Sciences, School of Technology, Environments and Design, University of Tasmania, Churchill Ave, Hobart, TAS 7005, Australia"}]},{"given":"Asim","family":"Anees","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Tasmania, Churchill Ave, Hobart, TAS 7005, Australia"},{"name":"Data Scientist Group, ProCan, Children\u2019s Medical Research Institute, 214 Hawkesbury Road, Westmead, NSW 2145, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4834-5641","authenticated-orcid":false,"given":"Brett","family":"Bryan","sequence":"additional","affiliation":[{"name":"Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, 221 Burwood Hwy, Burwood, VIC 3125, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. 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