An Open-Set Recognition Approach for SAR Targets Using Only Classification Scores

Qian Sun, Shichao Chen, Lirong Wu, Jia Su, Mingliang Tao, Ming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Focusing on the open set recognition problem of Synthetic aperture radar (SAR) targets, a simple and robust openset recognition approach using only a simple convolutional neural classification network is proposed in this paper. The proposed approach constructs the D-SCORE feature and uses the statistical method to model the D-SCORE obtained in the training phase, so as to obtain the threshold for identifying the known and unknown classes, and ultimately realizes the open-set recognition of SAR targets. Experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed approach achieves better open-set recognition performance.

Original languageEnglish
Title of host publication2024 4th URSI Atlantic Radio Science Meeting, AT-RASC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789463968102
DOIs
StatePublished - 2024
Event4th URSI Atlantic Radio Science Meeting, AT-RASC 2024 - Meloneras, Spain
Duration: 19 May 202424 May 2024

Publication series

Name2024 4th URSI Atlantic Radio Science Meeting, AT-RASC 2024

Conference

Conference4th URSI Atlantic Radio Science Meeting, AT-RASC 2024
Country/TerritorySpain
CityMeloneras
Period19/05/2424/05/24

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