NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing

Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. Model-based unmixing methods depend on well-designed iterative optimization algorithms, which is usually time consuming. Learning-based methods perform unmixing in a data-driven manner but hea...

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Bibliographic Details

Published in:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1865 - 1869
Main Authors:	Xiong, Fengchao, Zhou, Jun, Ye, Minchao, Lu, Jianfeng, Qian, Yuntao
Format:	Conference Proceeding
Language:	English
Published:	IEEE 06.06.2021
Subjects:	autoencoder Decoding Hyperspectral unmixing Learning systems model-based neural network sparse coding Sparse matrices Speech coding Speech processing Training
ISSN:	2379-190X
Online Access:	Get full text
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