Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation.
IEEE Transactions on Geoscience and Remote Sensing(2017)
摘要
This paper proposes a spectral-spatial classification algorithm based on principal components (PCs)-based smooth ordering and multiple 1-D interpolation, which can alleviate the general classification problems effectively. Because of the characteristics of hyperspectral image, there always exist easily separable samples (ESSs) and difficultly separable samples (DSSs) in view of the different sets ...
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关键词
Hyperspectral imaging,Feature extraction,Spread spectrum communication,Interpolation,Principal component analysis,Training
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