Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery
文献类型：SCI/SSCI论文
| 作者 | Guo X.; Huang, X.; Zhang, L. F.; Zhang, L. P.; Plaza, A.; Benediktsson, J. A. |
| 发表日期 | 2016 |
| 关键词 | Classification dimensionality reduction feature extraction hyperspectral support tensor machine (STM) support vector machine (SVM) tensor land-cover classification principal component analysis dimensionality reduction spatial classification discriminant-analysis gait recognition vector machines training data classifiers features |
| 英文摘要 | 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. |
| 出处 | Ieee Transactions on Geoscience and Remote Sensing |
| 卷 | 54 |
| 期 | 6 |
| 页 | 3248-3264 |
| 语种 | 英语 |
| ISSN号 | 0196-2892 |
| DOI标识 | 10.1109/tgrs.2016.2514404 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/42721]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Guo X.,Huang, X.,Zhang, L. F.,et al. Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery. 2016. |
入库方式： OAI收割
来源：地理科学与资源研究所
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