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

Gu, Xiaowei and Angelov, Plamen Parvanov and Zhang, Ce and Atkinson, Peter Michael (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

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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:
Journal Article
Journal or Publication Title:
IEEE Geoscience and Remote Sensing Letters
Additional Information:
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
89452
Deposited By:
Deposited On:
04 Jan 2018 14:10
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Mar 2020 05:26