@inproceedings{0595ffe80e1148bdbd9a194505720302,
title = "Semi-supervised hyperspectral image classification with multiscale kernels",
abstract = "In this letter, we propose a novel Laplacian least squares support vector machine in sum space (Lap-LS-SVM-SS) for semi-supervised HIC with multiscale kernels, which can simultaneously deal with the high- and low- frequency components of the target classification function with small and large scale kernels, respectively. The proposed method can solve the multi-class problem of hyperspectral dataset directly. The experimental results on AVIRIS Indian Pines dataset demonstrate that Lap-LS-SVM-SS yields superior classification performance over the conventional semi-supervised classifiers (e.g., LapSVM) in challenging small training labeled samples and a large amount of unlabeled samples.",
author = "Li Cui and Lu Liu and Chen, \{Di Rong\}",
note = "Publisher Copyright: {\textcopyright} 2018 Author(s).; 2018 International Conference on Civil, Mechanical and Material Engineering, ICCMME 2018 ; Conference date: 16-03-2018 Through 18-03-2018",
year = "2018",
month = jun,
day = "4",
doi = "10.1063/1.5041411",
language = "英语",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Jongwon Jung and Dongkeon Kim",
booktitle = "International Conference on Civil, Mechanical and Material Engineering, ICCMME 2018",
}