{"title":"基于自适应加权决策融合的多视点SAR目标识别方法","authors":"Tingwei Zhang","doi":"10.1080/2150704x.2023.2277157","DOIUrl":null,"url":null,"abstract":"ABSTRACTSynthetic aperture radar (SAR) provides high-resolution observations day and night, whose resulting images can be interpreted for different applications. For the SAR automatic target recognition (ATR) problem, this letter proposes a multi-view method based on adaptive decision fusion. The joint sparse representation (JSR) model is first employed to classify the multiple views. For the output decisions from different views, adaptive weights are determined based on Shannon entropy theory. The resulting weights are used for decision fusion to linearly combine the individual decisions from different SAR images to determine the target label. The MSTAR dataset is used for the experiments, on which both the standard operating condition (SOC) and two representative extended operating conditions (EOCs) are setup. By comparison with several state-of-the-art multi-view SAR ATR methods, the validity and robustness of the proposed method can be effectively confirmed.KEYWORDS: SARtarget recognitionjoint sparse representationadaptive weightsdecision fusion Disclosure statementNo potential conflict of interest was reported by the author.","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Multi-view SAR target recognition method based on adaptive weighted decision fusion\",\"authors\":\"Tingwei Zhang\",\"doi\":\"10.1080/2150704x.2023.2277157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTSynthetic aperture radar (SAR) provides high-resolution observations day and night, whose resulting images can be interpreted for different applications. For the SAR automatic target recognition (ATR) problem, this letter proposes a multi-view method based on adaptive decision fusion. The joint sparse representation (JSR) model is first employed to classify the multiple views. For the output decisions from different views, adaptive weights are determined based on Shannon entropy theory. The resulting weights are used for decision fusion to linearly combine the individual decisions from different SAR images to determine the target label. The MSTAR dataset is used for the experiments, on which both the standard operating condition (SOC) and two representative extended operating conditions (EOCs) are setup. By comparison with several state-of-the-art multi-view SAR ATR methods, the validity and robustness of the proposed method can be effectively confirmed.KEYWORDS: SARtarget recognitionjoint sparse representationadaptive weightsdecision fusion Disclosure statementNo potential conflict of interest was reported by the author.\",\"PeriodicalId\":49132,\"journal\":{\"name\":\"Remote Sensing Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/2150704x.2023.2277157\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/2150704x.2023.2277157","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
A Multi-view SAR target recognition method based on adaptive weighted decision fusion
ABSTRACTSynthetic aperture radar (SAR) provides high-resolution observations day and night, whose resulting images can be interpreted for different applications. For the SAR automatic target recognition (ATR) problem, this letter proposes a multi-view method based on adaptive decision fusion. The joint sparse representation (JSR) model is first employed to classify the multiple views. For the output decisions from different views, adaptive weights are determined based on Shannon entropy theory. The resulting weights are used for decision fusion to linearly combine the individual decisions from different SAR images to determine the target label. The MSTAR dataset is used for the experiments, on which both the standard operating condition (SOC) and two representative extended operating conditions (EOCs) are setup. By comparison with several state-of-the-art multi-view SAR ATR methods, the validity and robustness of the proposed method can be effectively confirmed.KEYWORDS: SARtarget recognitionjoint sparse representationadaptive weightsdecision fusion Disclosure statementNo potential conflict of interest was reported by the author.
期刊介绍:
Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.