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

Name: Unsupervised Computer Vision for Aerospace Systems: Spacecraft Pose Estimation to Infrastructure Health Monitoring
Brand: Springer
SKU: 9789819500222
Price: 14690.0 INR
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by Zhaoxiang Zhang

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ISBN13: 9789819500222
Binding: Hardcover
Subject: N/A
Publisher: Springer
Publisher Imprint: Springer
Publication Date: January 1, 2025
Pages: 207
Original Price: EUR 199.99
Language: English
Edition: N/A
Item Weight: 450 grams
BISAC Subject(s): Artificial Intelligence / Computer Vision & Pattern Recognition

About The Book

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.

About The Author

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.

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