Skip to main content

A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification

Gu, Xiaowei, Angelov, Plamen P. (2019) A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification. In: INNS Big Data and Deep Learning conference 2019. 1. pp. 257-266. Springer, Cham ISBN 978-3-030-16840-7. E-ISBN 978-3-030-16841-4. (doi:10.1007/978-3-030-16841-4_27) (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:90197)

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/10.1007/978-3-030-16841-4_27

Abstract

This paper proposes a new approach that is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene classification. The proposed approach employs a pre-trained deep convoluational neural network as the feature descriptor to extract high-level discriminative semantic features from the sub-regions of the remote sensing images. This approach is able to self-organize a set of prototype-based IF...THEN rules from few labeled training images through an efficient supervised initialization process, and continuously self-updates the rule base with the unlabeled images in an unsupervised, autonomous, transparent and human-interpretable manner. Highly accurate classification on the unlabeled images is performed at the end of the learning process. Numerical examples demonstrate that the proposed approach is a strong alternative to the state-of-the-art ones.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/978-3-030-16841-4_27
Uncontrolled keywords: Deep rule-based; Remote sensing scene classification; Semi-supervised learning
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 10:48 UTC
Last Modified: 15 Sep 2021 16:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90197 (The current URI for this page, for reference purposes)
  • Depositors only (login required):