Abstract
In recent years, the support vector machines (SVMs) have been very successful in remote sensing image classification, particularly when dealing with high-dimensional data and limited training samples. Nevertheless, the vector-based feature alignment of the SVM can lead to an information loss in representation of hyperspectral images, which intrinsically have a tensor-based data structure. In this paper, a new multiclass support tensor machine (STM) is specifically developed for hyperspectral image classification. Our newly proposed STM processes the hyperspectral image as a data cube and then identifies the information classes in tensor space. The multiclass STM is developed from a set of binary STM classifiers using the one-against-one parallel strategy. As a part of our tensor-based processing chain, a multilinear principal component analysis (MPCA) is used for preprocessing, in order to reduce the tensorial data redundancy and, at the same time, preserve the tensorial structure information in sparse and high-order subspaces. As a result, the contributions of this work are twofold: a new multiclass STM model for hyperspectral image classification is developed, and a tensorial image interpretation framework is constructed, which provides a system consisting of tensor-based feature representation, feature extraction, and classification. Experiments with four hyperspectral data sets, covering agricultural and urban areas, are conducted to validate the effectiveness of the proposed framework. Our experimental results show that the proposed STM and MPCA-STM can achieve better results than traditional SVM-based classifiers.
Original language | English |
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Article number | 7389377 |
Pages (from-to) | 3248-3264 |
Number of pages | 17 |
Journal | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 54 |
Issue number | 6 |
DOIs | |
Publication status | Published - Jun 2016 |
Bibliographical note
Funding Information:This work was supported in part by the National Natural Science Foundation of China under Grant 91338111, by the China National Science Fund for Excellent Young Scholars under Grant 41522110, by the Foundation for the Author of National Excellent Doctoral Dissertation of China under Grant 201348, and by the China Postdoctoral Science Foundation under Grant 2015M581162.
Publisher Copyright:
© 2015 IEEE.
Other keywords
- Classification
- dimensionality reduction
- feature extraction
- hyperspectral
- support tensor machine (STM)
- support vector machine (SVM)
- tensor