A Deep Rule-based Approach for Satellite Scene Image Analysis

Gu, Xiaowei and Angelov, Plamen Parvanov (2018) A Deep Rule-based Approach for Satellite Scene Image Analysis. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, JPN, pp. 2778-2783. ISBN 9781538666517

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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.

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Contribution in Book/Report/Proceedings
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ID Code:
126352
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Deposited On:
20 Jul 2018 15:20
Refereed?:
Yes
Published?:
Published
Last Modified:
09 Jul 2020 00:03