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Unsupervised computer vision for aerospace systems = spacecraft pose estimation to infrastructure health monitoring /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Unsupervised computer vision for aerospace systems/ by Zhaoxiang Zhang.
其他題名:	spacecraft pose estimation to infrastructure health monitoring /
作者:	Zhang, Zhaoxiang.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	vii, 200 p. :ill., digital ;24 cm.
內容註:	Introduction -- Jitter Estimation and Compensation in Spacecraft System -- Pose Estimation and Tracking for Space Objects -- Unsupervised Domain Adaptation for Autonomous Perception -- Safety Inspection of Aerospace Infrastructure -- Future Directions.
Contained By:	Springer Nature eBook
標題:	Computer vision. -
電子資源:	https://doi.org/10.1007/978-981-95-0023-9
ISBN:	9789819500239

Unsupervised computer vision for aerospace systems = spacecraft pose estimation to infrastructure health monitoring /
Zhang, Zhaoxiang.

Unsupervised computer vision for aerospace systemsspacecraft pose estimation to infrastructure health monitoring /[electronic resource] :by Zhaoxiang Zhang. - Singapore :Springer Nature Singapore :2025. - vii, 200 p. :ill., digital ;24 cm. - Scientific computation,2198-2589. - Scientific computation..

Introduction -- Jitter Estimation and Compensation in Spacecraft System -- Pose Estimation and Tracking for Space Objects -- Unsupervised Domain Adaptation for Autonomous Perception -- Safety Inspection of Aerospace Infrastructure -- Future Directions.

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.

ISBN: 9789819500239

Standard No.: 10.1007/978-981-95-0023-9doiSubjects--Topical Terms:

540671
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Unsupervised computer vision for aerospace systems = spacecraft pose estimation to infrastructure health monitoring /
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