A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication

Xiaowei Gu, Plamen Parvanov Angelov

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


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.
Original languageEnglish
Title of host publication2019 INNS Big Data and Deep Learning
Subtitle of host publicationINNSBDDL 2019 conference
Publication statusPublished - 03 Apr 2019
Externally publishedYes


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