Open Set Recognition and Category Discovery Framework for SAR Target Classification Based on K-Contrast Loss and Deep Clustering

Chen, Mingyao; Xia, Jing-Yuan; Liu, Tianpeng; Liu, Li; Liu, Yongxiang

doi:10.1109/jstars.2024.3353453

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

2024

DOI: 10.1109/jstars.2024.3353453

|View full text |Cite

Open Set Recognition and Category Discovery Framework for SAR Target Classification Based on K-Contrast Loss and Deep Clustering

Mingyao Chen,

Jing-Yuan Xia,

Tianpeng Liu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

LiOSR-SAR: Lightweight Open-Set Recognizer for SAR Imageries

Yang,

Gu,

Xin

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Open-set recognition (OSR) from synthetic aperture radar (SAR) imageries plays a crucial role in maritime and terrestrial monitoring. Nevertheless, numerous deep learning-based SAR classifiers struggle with unknown targets outside of the training dataset, leading to a dilemma, namely that a large model is difficult to deploy, while a smaller one sacrifices accuracy. To address this challenge, the novel “LiOSR-SAR” lightweight recognizer is proposed for OSR in SAR imageries. It incorporates the compact attribute focusing and open-prediction modules, which collectively optimize its lightweight structure and high accuracy. To validate LiOSR-SAR, “fast image simulation using bidirectional shooting and bouncing ray (FIS-BSBR)” is exploited to construct the corresponding dataset. It enhances the details of targets for more accurate recognition significantly. Extensive experiments show that LiOSR-SAR achieves remarkable recognition accuracies of 97.9% and 94.1% while maintaining a compact model size of 7.5 MB, demonstrating its practicality and efficiency.

show abstract

LiOSR-SAR: Lightweight Open-Set Recognizer for SAR Imageries

Yang,

Gu,

Xin

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Open Set Recognition and Category Discovery Framework for SAR Target Classification Based on K-Contrast Loss and Deep Clustering

Cited by 1 publication

References 45 publications

LiOSR-SAR: Lightweight Open-Set Recognizer for SAR Imageries

LiOSR-SAR: Lightweight Open-Set Recognizer for SAR Imageries

Contact Info

Product

Resources

About