Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples

Combined Spatial-Spectral Schroedinger Eigenmaps with Multiple Kernel Learning for Hyperspectral Image Classification Using a Low Number of Training Samples

The classification of hyperspectral images is one of the most popular fields in remote sensing applications. It should be noted that spectral and spatial features have critical roles in this research area. This paper proposes a method based on spatial-spectral Schroedinger eigenmaps (SSSE) and multi...

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Bibliographic Details
Main Authors:	Shirin Hassanzadeh, Habibollah Danyali, Mohammad Sadegh Helfroush
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2022-09-01
Series:	Canadian Journal of Remote Sensing
Online Access:	http://dx.doi.org/10.1080/07038992.2021.1978840

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