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

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>03/2018
<mark>Journal</mark>IEEE Geoscience and Remote Sensing Letters
Issue number3
Volume15
Number of pages5
Pages (from-to)345-349
StatePublished
Early online date5/02/18
Original languageEnglish

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.

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©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.