Share Email Print
cover

Proceedings Paper

Graph fusion based hyperspectral image classification
Author(s): Haokun Luo; Lin He; Long Yu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In hyperspectral image classification, small number of labeled samples versus high dimensional data is one of major challenges. Semi-supervised learning has shown potential to relieve the dilemma. Compared with its supervised learning counterpart, semi-supervised learning exploits both intrinsic structure of labeled and unlabeled samples. In this work, we proposed a graph-fusion based semi-supervised learning method for hyperspectral image classification. More specially, two graphs are constructed from spectral-spatial Gabor features and original spectral signatures, respectively, and then are integrated using an affine combination. Experimental results from an AVIRIS hyperspectral dataset verify the excellent classification performance of our method.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1143202 (14 February 2020); doi: 10.1117/12.2535913
Show Author Affiliations
Haokun Luo, South China Univ. of Technology (China)
Lin He, South China Univ. of Technology (China)
Long Yu, South China Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11432:
MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Zhiguo Cao; Jie Ma; Zhong Chen; Yu Shi, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray