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- W1994614669 abstract "Hyperspectral remote sensing image classification is a challenging task in remote sensing applications because thisimage always has some information redundancy and is easy to be affected by noise or lack of the separability. A semi-supervisedclassification method based on principal component analysis (PCA) method and kernel fuzzy C-means(KFCM) algorithm for hyperspectral remote sensing image is proposed in this paper. First the PCA method finds aneffective representation of spectral signature in a reduced dimensional feature space. Then a semi-supervised kernel-basedFCM algorithm, called SSKFCM algorithm by introducing semi-supervised learning technique and the kernel tricksimultaneously into conventional fuzzy C-means algorithm, is introduced to classify the feature vectors. Finallynumerical experiments are conducted on a hyperspectral remote sensing image that provides digital images of 80 spectralbands with wavelength rang from 455 nm to 1642 nm. Classification performance is estimated by classification accuracyand kappa coefficient. The simulation results show that the proposed approach can be effectively applied tohyperspectral remote sensing image classification." @default.
- W1994614669 created "2016-06-24" @default.
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- W1994614669 date "2008-10-31" @default.
- W1994614669 modified "2023-09-23" @default.
- W1994614669 title "Semi-supervised classification for hyperspectral remote sensing image based on PCA and kernel FCM algorithm" @default.
- W1994614669 doi "https://doi.org/10.1117/12.813255" @default.
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