Graph Attention Convolutional Autoencoder-Based Unsupervised Nonlinear Unmixing for Hyperspectral Images

Hyperspectral unmixing has received increasing attention as a technique for estimating endmember spectra and fractional abundances of land covers. Encoding high-dimensional hyperspectral data into a low-dimensional latent space to generate reasonable abundances, autoencoder (AE) has shown its great...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 16; S. 1 - 11
Hauptverfasser:	Jin, Danni, Yang, Bin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Autoencoder bilinear mixture model Convolution Convolutional neural networks Decoding Feature extraction graph attention convolutional network Hyperspectral imaging hyperspectral remote sensing imagery Mathematical models Mixing Mixture models nonlinear spectral unmixing Spatial data Spectra Synthetic data
ISSN:	1939-1404, 2151-1535
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!