Endmember extraction and abundance estimation algorithm based on double-compressed sampling-Reference-Cited by-同舟云学术

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Endmember extraction and abundance estimation algorithm based on double-compressed sampling

Published:2024-08-02 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Wang Li,Bi Yang,Wang Wei,Li Junfang

Funder

National Natural Science Foundation of China

Key Research and Development Program of Shaanxi

Research and Development Program of Shaanxi

Key Research and Development Projects of Shaanxi Province

Aeronautical Science Foundation

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-68382-y.pdf

Reference69 articles.

1. Aburaed, N. et al. A review of spatial enhancement of hyperspectral remote sensing imaging techniques. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16, 2275–2300. https://doi.org/10.1109/JSTARS.2023.3242048 (2023).

2. Wang, B. et al. Multichannel interconnection decomposition for hyperspectral LiDAR waveforms detected from over 500 m. IEEE Trans. Geosci. Remote Sens. 60, 1–14. https://doi.org/10.1109/TGRS.2021.3108160 (2022).

3. Chang, C. I. Hyperspectral anomaly detection: A dual theory of hyperspectral target detection. IEEE Trans. Geosci. Remote Sens. 60, 1–20. https://doi.org/10.1109/TGRS.2021.3086768 (2022).

4. Freitas, S., Silva, H. & Silva, E. Remote hyperspectral imaging acquisition and characterization for marine litter detection. Remote Sens. 13(13), 2536. https://doi.org/10.3390/rs13132536 (2021).

5. Matteoli, S. & Theiler, J. Bayesian target detection algorithms for solid subpixel targets in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 61, 1–14. https://doi.org/10.1109/TGRS.2023.3292067 (2023).

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