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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
