Locally Homogeneous Covariance Matrix Representation for Hyperspectral Image Classification

Locally Homogeneous Covariance Matrix Representation for Hyperspectral Image Classification

Combining spectralandspatial information has been proven to be an effective way for hyperspectral image (HSI) classification. However, making full use of spectral–spatial information of HSI still remains an open problem, especially when only a small number of labeled samples are available...

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Bibliographic Details
Main Authors: Xinyu Zhang, Yantao Wei, Huang Yao, Zhijing Ye, Yicong Zhou, Yue Zhao
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Covariance matrix (CM)
entropy rate superpixel (ERS) segmentation
feature extraction
hyperspectral image (HSI) classification
Online Access:https://ieeexplore.ieee.org/document/9531408/

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