Kernel Sparse Subspace Clustering with Total Variation Denoising for Hyperspectral Remote Sensing Images

Jorge Bacca; Carlos Hinojosa; Henry Argüello

doi:10.1364/math.2017.mtu4c.5

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Kernel Sparse Subspace Clustering with Total Variation Denoising for Hyperspectral Remote Sensing Images

Jorge Bacca Carlos Hinojosa Henry Argüello

Year: 2017 Pages: MTu4C.5-MTu4C.5

DOI: 10.1364/math.2017.mtu4c.5

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Abstract

This paper proposes a new hyperspectral image subspace clustering framework which adds a total variation denoising constraint in order to improve the similarity between data points from the same subspace.

Keywords:

Hyperspectral imaging Kernel (algebra) Pattern recognition (psychology) Cluster analysis Subspace topology Artificial intelligence Computer science Constraint (computer-aided design) Noise reduction Similarity (geometry) Mathematics Image (mathematics)

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Topics

Remote-Sensing Image Classification

Physical Sciences → Engineering → Media Technology

Remote Sensing and Land Use

Physical Sciences → Earth and Planetary Sciences → Atmospheric Science

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Kernel Sparse Subspace Clustering with Total Variation Denoising for Hyperspectral Remote Sensing Images

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