One-Step Generalized Likelihood Ratio Test for Subpixel Target Detection in Hyperspectral Imaging
Author:
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9097824/08964590.pdf?arnumber=8964590
Cited by 32 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Point-Set Topology-Based Information Entropy Estimation Method for Hyperspectral Target Detection;IEEE Transactions on Geoscience and Remote Sensing;2024
2. Level-Wise Band-Partition-Based Hierarchical Representation Residual Feature Learning for Hyperspectral Target Detection;2023 IEEE 19th International Conference on Automation Science and Engineering (CASE);2023-08-26
3. Rethinking Representation Learning-Based Hyperspectral Target Detection: A Hierarchical Representation Residual Feature-Based Method;Remote Sensing;2023-07-19
4. Bayesian vs Generalized Likelihood Ratio Detection of Solid Sub-Pixel Targets;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
5. Adaptive Detection of Multiple Sub-Pixel Targets in Hyperspectral Systems;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
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