Unsupervised Computer Vision for Aerospace Systems : Spacecraft Pose Estimation to Infrastructure Health Monitoring

Zhaoxiang Zhang

ISBN 10: 9819500222 / ISBN 13: 9789819500222

Editore: Springer, Springer, 2025

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Druck auf Anfrage Neuware - Printed after ordering - This book addresses perception and monitoring challenges in aerospace systems by employing innovative unsupervised learning techniques, thereby providing solutions for scenarios characterized by limited labelled data or dynamic environments. It explores practical methods such as domain adaptation for cross-modal pose estimation, causal inference for point cloud segmentation, and lightweight vision models optimized for edge computing. Key features include algorithm flowcharts, performance comparison tables, and real-world case studies covering planetary crater detection and spacecraft pose estimation. The integration of generative adversarial networks (GANs) for satellite jitter estimation and multistep adaptation strategies for defect detection offers actionable insights, supported by real industrial datasets, embedded hardware schematics, software code snippets, and optimization guidelines for real-time deployment. Engineers and researchers will obtain tools to enhance robustness across modalities and domains, ensuring generalizability in resource-constrained settings. This book serves as a valuable reference for aerospace engineers, computer vision specialists, and remote sensing practitioners and also empowers aerospace infrastructure inspectors adopting advanced vision technologies.

Codice articolo 9789819500222

Dati bibliografici

• Titolo: Unsupervised Computer Vision for Aerospace Systems : Spacecraft Pose Estimation to Infrastructure Health Monitoring
• Editore: Springer, Springer
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 9819500222
• ISBN 13: 9789819500222
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 17 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 17 millimetri di profondità
• Peso: 519 grammi

Descrizione articolo

Riassunto

This book addresses perception and monitoring challenges in aerospace systems by employing innovative unsupervised learning techniques, thereby providing solutions for scenarios characterized by limited labelled data or dynamic environments. It explores practical methods such as domain adaptation for cross-modal pose estimation, causal inference for point cloud segmentation, and lightweight vision models optimized for edge computing. Key features include algorithm flowcharts, performance comparison tables, and real-world case studies covering planetary crater detection and spacecraft pose estimation. The integration of generative adversarial networks (GANs) for satellite jitter estimation and multistep adaptation strategies for defect detection offers actionable insights, supported by real industrial datasets, embedded hardware schematics, software code snippets, and optimization guidelines for real-time deployment. Engineers and researchers will obtain tools to enhance robustness across modalities and domains, ensuring generalizability in resource-constrained settings. This book serves as a valuable reference for aerospace engineers, computer vision specialists, and remote sensing practitioners and also empowers aerospace infrastructure inspectors adopting advanced vision technologies.

Informazioni sull?autore

Zhaoxiang Zhang received his Ph.D. in 2020 from the Institute of Satellite Technology, School of Astronautics, Harbin Institute of Technology, China. Since 2020, he has been an associate professor at the Institute of Unmanned System Technology, Northwestern Polytechnical University, where his research focuses on unmanned system technology, unsupervised learning, and aerospace image processing. His landmark achievements have been successfully applied to the development of reconnaissance equipment, including a high-altitude UAV, an in-service medium-altitude UAV, and the CW-25 industrial UAV, enhancing reconnaissance and positioning performance as well as improving image interpretation systems.