DNGAE: Deep Neighborhood Graph Autoencoder for Robust Blind Hyperspectral Unmixing
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-41456-5_7
Reference19 articles.
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2. Ayma Quirita, V.A., da Costa, G.A.O.P., Beltrán, C.: A distributed N-FINDR cloud computing-based solution for endmembers extraction on large-scale hyperspectral remote sensing data. Remote Sens. 14(9) (2022)
3. Alves, J.M.R., Nascimento, J.M.P., Plaza, A., Sanchez, S., Bioucas-Dias, J.M., Silva, V.: Vertex component analysis GPU-based implementation for hyperspectral unmixing. In: 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Shanghai, China, pp. 1–4 (2012)
4. Li, J., Agathos, A., Zaharie, D., Bioucas-Dias, J.M., Plaza, A., Li, X.: Minimum volume simplex analysis: a fast algorithm for linear hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 53(9), 5067–5082 (2015)
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1. Graph feature fusion driven by deep autoencoder for advanced hyperspectral image unmixing;Knowledge-Based Systems;2024-09
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