Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer
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 geode...
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doaj-a10aa4d8ce2446e6a3926b7fd37562922020-11-24T21:17:47ZengMDPI AGRemote Sensing2072-42922016-03-018323410.3390/rs8030234rs8030234Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature TransferAlim Samat0Paolo Gamba1Jilili Abuduwaili2Sicong Liu3Zelang Miao4State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, ItalyState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaCollege of Surveying and Geoinformatics, Tongji University, Shanghai 200092, ChinaDepartment of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, ChinaIn 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.http://www.mdpi.com/2072-4292/8/3/234transfer learningdomain adaptationgeodesic flow kernel support vector machinerandomized nonlinear principal component analysisfeature transferimage classification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alim Samat Paolo Gamba Jilili Abuduwaili Sicong Liu Zelang Miao |
spellingShingle |
Alim Samat Paolo Gamba Jilili Abuduwaili Sicong Liu Zelang Miao Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer Remote Sensing transfer learning domain adaptation geodesic flow kernel support vector machine randomized nonlinear principal component analysis feature transfer image classification |
author_facet |
Alim Samat Paolo Gamba Jilili Abuduwaili Sicong Liu Zelang Miao |
author_sort |
Alim Samat |
title |
Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer |
title_short |
Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer |
title_full |
Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer |
title_fullStr |
Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer |
title_full_unstemmed |
Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer |
title_sort |
geodesic flow kernel support vector machine for hyperspectral image classification by unsupervised subspace feature transfer |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2016-03-01 |
description |
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. |
topic |
transfer learning domain adaptation geodesic flow kernel support vector machine randomized nonlinear principal component analysis feature transfer image classification |
url |
http://www.mdpi.com/2072-4292/8/3/234 |
work_keys_str_mv |
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1726012125398171648 |