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

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...

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Bibliographic Details
Main Authors: Han Zhai, Hongyan Zhang, Xiong Xu, Liangpei Zhang, Pingxiang Li
Format: Article
Language:English
Published: MDPI AG 2017-04-01
Series:Remote Sensing
Subjects:
hyperspectral images
subspace clustering
nonlinear techniques
kernels
spatial max pooling
Online Access:http://www.mdpi.com/2072-4292/9/4/335

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