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    • Deep unfolding network for hyperspectral anomaly detection

    • In the field of hyperspectral anomaly detection, researchers have combined low rank representation models with deep learning techniques to propose LRR Net, which effectively improves detection accuracy and generalization.
    • Vol. 28, Issue 1, Pages: 69-77(2024)   

      Received:15 March 2023

      Published:07 January 2024

    • DOI: 10.11834/jrs.20233075     

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  • Li C Y,Hong D F and Zhang B. 2024. Deep unfolding network for hyperspectral anomaly detection. National Remote Sensing Bulletin, 28(1):69-77 DOI: 10.11834/jrs.20233075.
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相关作者

Bo DU 武汉大学 计算机学院
Liangpei ZHANG 武汉大学 测绘遥感信息工程国家重点实验室
Dehui ZHU 武汉大学 测绘遥感信息工程国家重点实验室
Gang YANG 宁波大学 地理与空间信息技术系
Dianfa ZHANG 宁波大学 地理与空间信息技术系
Fei LI 宁波大学 地理与空间信息技术系
Weiwei SUN 宁波大学 地理与空间信息技术系
Yi CEN 中国科学院遥感与数字地球研究所

相关机构

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
School of Computer Science, Wuhan University
Department of Geography and Spatial Information Techniques, Ningbo University
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
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