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...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2022-09-01
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Series: | Canadian Journal of Remote Sensing |
Online Access: | http://dx.doi.org/10.1080/07038992.2021.1978840 |