Hyperspectral Image Clustering Based on Unsupervised Broad Learning

Due to the difficulty of labeling a large number of training samples of a hyperspectral image (HSI), unsupervised clustering methods have drawn great attention. The recently proposed broad learning (BL) can implement both linear and nonlinear mappings. However, the original BL is a supervised model....

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE geoscience and remote sensing letters Ročník 16; číslo 11; s. 1741 - 1745
Hlavní autori:	Kong, Yi, Cheng, Yuhu, Chen, C. L. Philip, Wang, Xuesong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Clustering Clustering methods Eigenvalues Eigenvalues and eigenfunctions Eigenvectors hyperspectral image Hyperspectral imaging Learning Linear programming manifold Manifolds Methods Objective function Regularization Sparse matrices Training unsupervised broad learning (UBL)
ISSN:	1545-598X, 1558-0571
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!