SLIC Superpixel-Based <i>l</i><sub>2,1</sub>-Norm Robust Principal Component Analysis for Hyperspectral Image Classification

SLIC Superpixel-Based <i>l</i><sub>2,1</sub>-Norm Robust Principal Component Analysis for Hyperspectral Image Classification

Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades. However, explosive growth in HSIs&#8217; scale and dimensions causes &#8220;Curse of dimensionality&#8222; and &#8220;Hughes phenomenon...

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Bibliographic Details
Main Authors: Baokai Zu, Kewen Xia, Tiejun Li, Ziping He, Yafang Li, Jingzhong Hou, Wei Du
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:Sensors
Subjects:
Hyperspectral Image
Robust Principal Component Analysis (RPCA)
Simple Linear Iterative Clustering (SLIC)
superpixel segmentation
Online Access:https://www.mdpi.com/1424-8220/19/3/479

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https://www.mdpi.com/1424-8220/19/3/479

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