Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 51, Issue 9, Pages 4800-4815Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2012.2230445
Keywords
Dimension reduction (DR); multilevel segmentation; semisupervised learning
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Funding
- National Basic Research Program of China (973 Program) [2011CB707105, 2012CB719905]
- National Natural Science Foundation of China [40930532, 61102128]
- Fundamental Research Funds for the Central Universities of the People's Republic of China
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This paper proposes a new semisupervised dimension reduction (DR) algorithm based on a discriminative locally enhanced alignment technique. The proposed DR method has two aims: to maximize the distance between different classes according to the separability of pairwise samples and, at the same time, to preserve the intrinsic geometric structure of the data by the use of both labeled and unlabeled samples. Furthermore, two key problems determining the performance of semisupervised methods are discussed in this paper. The first problem is the proper selection of the unlabeled sample set; the second problem is the accurate measurement of the similarity between samples. In this paper, multilevel segmentation results are employed to solve these problems. Experiments with extensive hyperspectral image data sets showed that the proposed algorithm is notably superior to other state-of-the-art dimensionality reduction methods for hyperspectral image classification.
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