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

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

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

48 Dyfyniadau (Scopus)
29 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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.
Iaith wreiddiolSaesneg
Tudalennau (o-i)345-349
Nifer y tudalennau5
CyfnodolynIEEE Geoscience and Remote Sensing Letters
Cyfrol15
Rhif cyhoeddi3
Dyddiad ar-lein cynnar05 Chwef 2018
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Maw 2018
Cyhoeddwyd yn allanolIe

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