Graph fusion based hyperspectral image classification

Haokun Luo; Lin He; Long Yu

doi:10.1117/12.2535913

14 February 2020 Graph fusion based hyperspectral image classification

Haokun Luo, Lin He, Long Yu

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 1143202 (2020) https://doi.org/10.1117/12.2535913
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Haokun Luo, Lin He, and Long Yu "Graph fusion based hyperspectral image classification", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1143202 (14 February 2020); https://doi.org/10.1117/12.2535913

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KEYWORDS

Hyperspectral imaging

Image classification

Feature extraction

Optical filters

Solid state lighting

Image fusion

Seaborgium

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