SSF-Net: A Spatial-Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing

In recent years, deep learning (DL) has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities. Particularly, the unsupervised unmixing method based on autoencoder (AE) has become a research hotspot. Most of the current AE unmixing network...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 17; S. 1 - 14
Hauptverfasser:	Wang, Bin, Yao, Huizheng, Song, Dongmei, Zhang, Jie, Gao, Han
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Attention autoencoder (AE) Data models Decoding Deep learning deep learning (DL) Electromagnetic scattering Feature extraction feature fusion Heterogeneity Hyperspectral imaging hyperspectral unmixing (HU) Information processing Machine learning Patchiness Spatial heterogeneity Task analysis
ISSN:	1939-1404, 2151-1535
Online-Zugang:	Volltext
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