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A Massively Parallel Deep Rule-Based Ensemble Classifier for Remote Sensing Scenes

Gu, Xiaowei, Angelov, Plamen P., Zhang, Ce, Atkinson, Peter M. (2018) A Massively Parallel Deep Rule-Based Ensemble Classifier for Remote Sensing Scenes. IEEE Geoscience and Remote Sensing Letters, 15 (3). pp. 345-349. ISSN 1545-598X. E-ISSN 1558-0571. (doi:10.1109/LGRS.2017.2787421) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90206)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/LGRS.2017.2787421

Abstract

In this letter, we propose a new approach for remote sensing scene classification by creating an ensemble of the recently introduced massively parallel deep (fuzzy) rule-based (DRB) classifiers trained with different levels of spatial information separately. Each DRB classifier consists of a massively parallel set of human-interpretable, transparent zero-order fuzzy IF...THEN... rules with a prototype-based nature. The DRB classifier can self-organize “from scratch” and self-evolve its structure. By employing the pretrained deep convolution neural network as the feature descriptor, the proposed DRB ensemble is able to exhibit human-level performance through a transparent and parallelizable training process. Numerical examples using benchmark data set demonstrate the superior accuracy of the proposed approach together with human-interpretable fuzzy rules autonomously generated by the DRB classifier.

Item Type: Article
DOI/Identification number: 10.1109/LGRS.2017.2787421
Uncontrolled keywords: Training; Image segmentation; Prototypes; Remote sensing; Semantics; Feature extraction; Sensors; Deep learning (DL); fuzzy rules; rule-based classifier; scene classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Sep 2021 13:09 UTC
Last Modified: 15 Sep 2021 15:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90206 (The current URI for this page, for reference purposes)
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