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Progress in Artificial Intelligence
Published in: Progress in Artificial Intelligence 2/2018

10-11-2017 | Regular Paper

Integration of fuzzy theory and particle swarm optimization for high-resolution satellite scene recognition

Authors: Linyi Li, Yun Chen, Tingbao Xu

Published in: Progress in Artificial Intelligence | Issue 2/2018

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Abstract

With the rapid development of satellite imaging technology, large amounts of satellite images with high spatial resolutions are now available. High-resolution satellite imagery provides rich texture and structure information, which in the meantime poses a great challenge for automatic satellite scene recognition. In this study, a novel integration method of fuzzy theory and particle swarm optimization (IFTPSO) is proposed to achieve an increased accuracy of satellite scene recognition (SSR) in high-resolution satellite imagery. The particle encoding, fitness function and swarm search strategy are designed for IFTPSO-SSR. The IFTPSO-SSR method was evaluated using the satellite scenes from QuickBird, IKONOS and ZY-3. IFTPSO-SSR outperformed three traditional recognition methods with the highest recognition accuracy. The parameter sensitivity of IFTPSO-SSR was also discussed. The proposed method of this study can enhance the performance of satellite scene recognition in high-resolution satellite imagery, and thereby advance the research and applications of artificial intelligence and satellite image analysis.
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Metadata
Title
Integration of fuzzy theory and particle swarm optimization for high-resolution satellite scene recognition
Authors
Linyi Li
Yun Chen
Tingbao Xu
Publication date
10-11-2017
Publisher
Springer Berlin Heidelberg
Published in
Progress in Artificial Intelligence / Issue 2/2018
Print ISSN: 2192-6352
Electronic ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-017-0139-z

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