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A Deep Rule-Based Approach for Satellite Scene Image Analysis

Gu, Xiaowei, Angelov, Plamen (2018) A Deep Rule-Based Approach for Satellite Scene Image Analysis. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). . pp. 2778-2783. IEEE ISBN 978-1-5386-6651-7. E-ISBN 978-1-5386-6650-0. (doi:10.1109/SMC.2018.00474) (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:90201)

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/10.1109/SMC.2018.00474

Abstract

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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/SMC.2018.00474
Uncontrolled keywords: Satellites; Image analysis; Semantics; Prototypes; Training; Image segmentation; Nickel; deep learning; deep fuzzy rule-based classifier; image analysis
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 12:47 UTC
Last Modified: 15 Sep 2021 15:59 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90201 (The current URI for this page, for reference purposes)
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