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Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Forthcoming
Publication date7/03/2019
Host publicationThe 2019 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Original languageEnglish

Abstract

In this paper, a new deep rule-based approach using high-level ensemble feature descriptor is proposed for aerial scene classification. By creating an ensemble of three pre-trained deep convolutional neural networks as the feature descriptor, the proposed approach is able to extract more discriminative representations from the local regions of aerial images. With a set of massively parallel IF…THEN rules built upon the prototypes identified through a self-organizing, nonparametric, transparent and highly human-interpretable learning process, the proposed approach is able to produce the state-of-the-art classification results on the unlabeled images outperforming the alternatives. Numerical examples on benchmark datasets demonstrate the strong performance of the proposed approach.

Bibliographic note

©2019 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.