國立虎尾科技大學 |

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Unsupervised computer vision for aerospace systems/ by Zhaoxiang Zhang.
Reminder of title:	spacecraft pose estimation to infrastructure health monitoring /
Author:	Zhang, Zhaoxiang.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	vii, 200 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Computer vision. -
Online resource:	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:

561800
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Unsupervised computer vision for aerospace systems = spacecraft pose estimation to infrastructure health monitoring /
LDR:02634nam a2200337 a 4500 001 1166362
003 DE-He213
005 20250806173749.0
006 m d
007 cr nn 008maaau
008 251217s2025 si s 0 eng d
020 $a 9789819500239 $q (electronic bk.)
020 $a 9789819500222 $q (paper)
024 7 $a 10.1007/978-981-95-0023-9 $2 doi
035 $a 978-981-95-0023-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1634
072 7 $a UYQV $2 bicssc
072 7 $a COM016000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 006.37 $2 23
090 $a TA1634 $b .Z63 2025
100 1 $a Zhang, Zhaoxiang. $e editor. $3 1268532
245 1 0 $a Unsupervised computer vision for aerospace systems $h [electronic resource] : $b spacecraft pose estimation to infrastructure health monitoring / $c by Zhaoxiang Zhang.
260 $a Singapore : $c 2025. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a vii, 200 p. : $b ill., digital ; $c 24 cm.
490 1 $a Scientific computation, $x 2198-2589
505 0 $a 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.
520 $a 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.
650 0 $a Computer vision. $3 561800
650 0 $a Aerospace engineering. $3 686400
650 0 $a Space vehicles $x Electronic equipment. $3 1086978
650 1 4 $a Computer Vision. $3 1127422
650 2 4 $a Aerospace Technology and Astronautics. $3 683885
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Artificial Intelligence. $3 646849
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Scientific computation. $3 891679
856 4 0 $u https://doi.org/10.1007/978-981-95-0023-9
950 $a Engineering (SpringerNature-11647)