Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation

Hyperspectral image (HSI) clustering is generally a challenging task because of the complex spectral-spatial structure. Based on the assumption that all the pixels are sampled from the union of subspaces, recent works have introduced a robust technique—the sparse subspace clustering (SSC) algorithm...

Full beskrivning

Bibliografiska uppgifter

Huvudupphov:	Han Zhai, Hongyan Zhang, Xiong Xu, Liangpei Zhang, Pingxiang Li
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	MDPI AG 2017-04-01
Serie:	Remote Sensing
Ämnen:	hyperspectral images subspace clustering nonlinear techniques kernels spatial max pooling
Länkar:	http://www.mdpi.com/2072-4292/9/4/335
Taggar:	Lägg till en tagg Inga taggar, Lägg till första taggen!