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Remote Sens. 2016, 8(3), 234; doi:10.3390/rs8030234

Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

1
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2
Chinese Academy of Sciences Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
3
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
4
College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China
5
Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao, Ioannis Gitas and Prasad S. Thenkabail
Received: 15 January 2016 / Revised: 1 March 2016 / Accepted: 4 March 2016 / Published: 16 March 2016

Abstract

In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA) in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM). To show the superior performance of the proposed approach, conventional support vector machines (SVMs) and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC), joint distribution adaptation (JDA), and joint transfer matching (JTM), are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA), randomized nonlinear principal component analysis (rPCA), factor analysis (FA) and non-negative matrix factorization (NNMF) are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images. View Full-Text
Keywords: transfer learning; domain adaptation; geodesic flow kernel support vector machine; randomized nonlinear principal component analysis; feature transfer; image classification transfer learning; domain adaptation; geodesic flow kernel support vector machine; randomized nonlinear principal component analysis; feature transfer; image classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Samat, A.; Gamba, P.; Abuduwaili, J.; Liu, S.; Miao, Z. Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer. Remote Sens. 2016, 8, 234.

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