Explicit Background Endmember Learning for Hyperspectral Anomaly Detection-Reference-Cited by-同舟云学术

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Explicit Background Endmember Learning for Hyperspectral Anomaly Detection

Published:2024 Issue: Volume:73 Page:1-17
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Li Kun¹^ORCID,An Wei²^ORCID,Wang Yingqian²^ORCID,Zhang Tingting³^ORCID,Qin Yao⁴^ORCID,Gao Tinghong¹^ORCID

Affiliation:

1. College of Big Data and Information Engineering, Guizhou University, Guiyang, China

2. College of Electronic Science and Technology, National University of Defense Technology, Changsha, China

3. Beijing Institute of Tracking and Telecommunication Technology, Beijing, China

4. Northwest Institute of Nuclear Technology, Xi’an, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10643713.pdf?arnumber=10643713

Reference80 articles.

1. Automated Hyperspectral Cueing for Civilian Search and Rescue

2. Locating the Oil Leakage on Power Equipment via Ultraviolet-Induced Hyperspectral Imaging Technology

3. Hyperspectral Anomaly Detection Based on Machine Learning: An Overview

4. Hyperspectral Anomaly Detection Using Quantum Potential Clustering

5. SSAPN: Spectral–Spatial Anomaly Perception Network for Unsupervised Vaccine Detection

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