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Dimensionality Reduction Techniques For Hyperspectral Image using Deep Learning
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
This Research proposal addresses the issues of dimension reduction algorithms in Deep Learning(DL) for Hyperspectral Imaging (HSI) classification, to reduce the size of training dataset and for feature extraction ICA(Independent Component Analysis) are adopted. The proposed algorithm evaluated uses real HSI data set. It shows that ICA gives the most optimistic presentation it shrinks off the feature occupying a small portion of all pixels distinguished from the noisy bands based on non Gaussian
doi:10.35940/ijitee.b1033.1292s319
fatcat:hillxk55sngt7o42xbtxpm4ea4