Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing Incorporating Endmember Independence
Author:
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9314330/09609564.pdf?arnumber=9609564
Cited by 8 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. Convex Geometry Based Endmember Extraction for Hyperspectral Images Classification;2023 13th International Conference on Information Science and Technology (ICIST);2023-12-08
2. GAUSS: Guided encoder - decoder Architecture for hyperspectral Unmixing with Spatial Smoothness;European Journal of Remote Sensing;2023-11-18
3. Sparse Nonnegative Matrix Factorization based on Double Low-Rank and Sparse Matrix Factorization Model for Spectral Variability;2023 7th International Symposium on Computer Science and Intelligent Control (ISCSIC);2023-10-27
4. Endmember Abundance Prediction in Hyperspectral Unmixing: The Impact of Endmember Extraction Algorithms and Self-Attention in Autoencoders;2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS);2023-08-25
5. Spatial Validation of Spectral Unmixing Results: A Systematic Review;Remote Sensing;2023-05-29
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