Perceptual Loss-Constrained Adversarial Autoencoder Networks for Hyperspectral Unmixing

Recently, the use of a deep autoencoder-based method in blind spectral unmixing has attracted great attention as the method can achieve superior performance. However, most autoencoder-based unmixing methods use non-structured reconstruction loss to train networks, leading to the ignorance of band-to...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 19; S. 1 - 5
Hauptverfasser:	Zhao, Min, Wang, Mou, Chen, Jie, Rahardja, Susanto
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Ablation Autoencoder Convolutional neural networks Decoding fine structure Frameworks generative adversarial network (GAN) Generators Hyperspectral imaging hyperspectral unmixing Image reconstruction Loss measurement Methods perceptual loss Training
ISSN:	1545-598X, 1558-0571
Online-Zugang:	Volltext
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