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A Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis

Gu, Xiaowei, Angelov, Plamen, Zhang, Ce, Atkinson, Peter (2021) A Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, . p. 1. ISSN 0162-8828. E-ISSN 1939-3539. (doi:10.1109/TPAMI.2020.3048268) (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:90290)

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/TPAMI.2020.3048268

Abstract

Large-scale {(large-area)}, fine spatial resolution satellite sensor images are valuable data sources for Earth observation while not yet fully exploited by research communities for practical applications. Often, such images exhibit highly complex geometrical structures and spatial patterns, and distinctive characteristics of multiple land-use categories may appear at the same region. Autonomous information extraction from these images is essential in the field of pattern recognition within remote sensing, but this task is extremely challenging due to the spectral and spatial complexity captured in satellite sensor imagery. In this research, a semi-supervised deep rule-based approach for satellite sensor image analysis (SeRBIA) is proposed, where large-scale satellite sensor images are analysed autonomously and classified into detailed land-use categories. Using an ensemble feature descriptor derived from pre-trained AlexNet and VGG-VD-16 models, SeRBIA is capable of learning continuously from both labelled and unlabelled images through self-adaptation without human involvement or intervention. Extensive numerical experiments were conducted on both benchmark datasets and real-world satellite sensor images to comprehensively test the validity and effectiveness of the proposed method. The novel information mining technique developed here can be applied to analyse large-scale satellite sensor images with high accuracy and interpretability, across a wide range of real-world applications.

Item Type: Article
DOI/Identification number: 10.1109/TPAMI.2020.3048268
Uncontrolled keywords: Satellites; Image segmentation; Feature extraction; Semantics; Semisupervised learning; Mathematical model; Prototypes; deep rule-based system; deep learning; satellite sensor image analysis; 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: 21 Sep 2021 15:10 UTC
Last Modified: 22 Sep 2021 09:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90290 (The current URI for this page, for reference purposes)
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