roodposhti-remotesensing-2019.pdf (3.67 MB)
A robust rule-based ensemble framework using mean-shift segmentation for hyperspectral image classification
journal contribution
posted on 2019-01-01, 00:00 authored by M S Roodposhti, A Lucieer, A Anees, Brett BryanBrett Bryan© 2019 by the authors. 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.
History
Journal
Remote SensingVolume
11Issue
17Article number
2057Pagination
1 - 20Publisher
MDPILocation
Basel, SwitzerlandPublisher DOI
Link to full text
eISSN
2072-4292Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalUsage metrics
Keywords
image classificationensemblemean-shiftentropyuncertainty mapScience & TechnologyLife Sciences & BiomedicinePhysical SciencesTechnologyEnvironmental SciencesGeosciences, MultidisciplinaryRemote SensingImaging Science & Photographic TechnologyEnvironmental Sciences & EcologyGeologySPECTRAL-SPATIAL CLASSIFICATIONLAND-COVERRANDOM FORESTFEATURE-EXTRACTIONBAND SELECTIONREGRESSIONALGORITHMGRADIENTMACHINESACCURACYArtificial Intelligence and Image Processing
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