第1个回答 2018-12-31
Abstract: Feature extraction is quite valuable for the mining and utilization ofvalid information in hyperspectral re- mote-sensing imaging and the increase ofsubsequent classified applications, For improving the dimension reduction
effect,a subspace-modulated kernel principal component analysis ( SM-KPCA ) method is proposed, With this method,the grouping natures ofhyperspectral data are integrated into a uniform kernel method framework and a subspacemodulated kernel is constructed,SMK( subspacemodulated kernel)achieves a sparse modulation on the --
spectral waveband by means offeature grouping; in addition,it is a data-adaptive kernel for measuring the nonlin- ear similarities among the hyperspectral data specimens, With the proposed method,AVI,IS( airborne visible infra- red imaging spectrometer) real hyperspectral imaging is applied for evaluation,Additionally,this method is com- pared with the conventional kernel method and the spectrally weighted kernel method,The experimental result