Kernel Low-Rank Entropic Component Analysis for Hyperspectral Image Classification

Kernel Low-Rank Entropic Component Analysis for Hyperspectral Image Classification

Principal component analysis (PCA) and its variations are still the primary tool for feature extraction (FE) in the remote sensing community. This is unfortunate, as there has been a strong argument against using PCA for this purpose due to its inherent linear properties and uninformative principal...

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Bibliographic Details
Main Authors: Chengzu Bai, Ren Zhang, Zeshui Xu, Baogang Jin, Jian Chen, Shuo Zhang, Longxia Qian
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Feature extraction (FE)
image classification
maximum entropy methods
Online Access:https://ieeexplore.ieee.org/document/9198067/

Internet

https://ieeexplore.ieee.org/document/9198067/

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