Nonlinear Unmixing of Hyperspectral Data via Deep Autoencoder Networks

Nonlinear spectral unmixing is an important and challenging problem in hyperspectral image processing. Classical nonlinear algorithms are usually derived based on specific assumptions on the nonlinearity. In recent years, deep learning shows its advantage in addressing general nonlinear problems. Ho...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 16; H. 9; S. 1467 - 1471
Hauptverfasser:	Wang, Mou, Zhao, Min, Chen, Jie, Rahardja, Susanto
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Autoencoder network Coders Current data Decoding Deep learning Hyperspectral imaging Image processing Machine learning Neural networks nonlinear spectral unmixing Nonlinear systems Nonlinearity Signal processing algorithms Training
ISSN:	1545-598X, 1558-0571
Online-Zugang:	Volltext
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