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Spectral-spatial classification integrating band selection for hyperspectral imagery with severe noise bands
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Spectral-spatial classification for hyperspectral imagery has been receiving much attention, since the detailed spectral and rich spatial information of hyperspectral images can be fully exploited to improve the classification accuracy. However, when the original hyperspectral images have very noisy bands, these bands may have an unfavorable impact on the classification, and are often discarded in advance based on expert knowledge. In this study, a spectral-spatial conditional random field
doi:10.1109/jstars.2020.2984568
fatcat:mszx2i4qmzdufmyqnl2jthhkxa