Adaptive locality preserving projection for hyperspectral image classification

Lin He; Xianjun Chen; Xiaofeng Xie; Haokun Luo

doi:10.1117/12.2535915

14 February 2020 Adaptive locality preserving projection for hyperspectral image classification

Lin He, Xianjun Chen, Xiaofeng Xie, Haokun Luo

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 1143203 (2020) https://doi.org/10.1117/12.2535915
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 relief 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

Lin He, Xianjun Chen, Xiaofeng Xie, and Haokun Luo "Adaptive locality preserving projection for hyperspectral image classification", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1143203 (14 February 2020); https://doi.org/10.1117/12.2535915

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