A Deep Rule-based Approach for Satellite Scene Image Analysis

Xiaowei Gu, Plamen Parvanov Angelov

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

3 Citations (SciVal)


Satellite scene images contain multiple sub-regions of different land use categories; however, traditional approaches usually classify them into a particular category only. In this paper, a new approach is proposed for automatically analyzing the semantic content of sub-regions of satellite images. At the core of the proposed approach is the recently introduced deep rule-based image classification method. The proposed approach includes a self-organizing set of transparent zero order fuzzy IF-THEN rules with human-interpretable prototypes identified from the training images and a pre-trained deep convolutional neural network as the feature descriptor. It requires a very short, nonparametric, highly parallelizable training process and can perform a highly accurate analysis on the semantic features of local areas of the image with the generated IF-THEN rules in a fully automatic way. Examples based on benchmark datasets demonstrate the validity and effectiveness of the proposed approach.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Subtitle of host publicationProceedings
Number of pages6
ISBN (Electronic)9781538666500
Publication statusPublished - 17 Jan 2019
Externally publishedYes

Publication series

NameProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018


  • deep fuzzy rule-based classifier
  • deep learning
  • image analysis


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