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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Huiwu Luo Yuan Yan Tang Yulong Wang Chunli Li Jianzhong Wang Tingbo Hu Hong Li |
| Copyright Year | 2015 |
| Description | Author affiliation: Nat. Univ. of Defense Technol., Changsha, China (Tingbo Hu) || Univ. of Macau, Macau, China (Huiwu Luo; Yuan Yan Tang; Yulong Wang; Chunli Li) || Huazhong Univ. of Sci. & Technol., Wuhan, China (Hong Li) || Sam Houston State Univ., Huntsville, TX, USA (Jianzhong Wang) |
| Abstract | In this paper, a novel classification paradigm, termed Spectral-Spatial One Dimensional Manifold Embedding (SS1DME), is proposed for classification of hyperspectral imagery (HSI). The proposed paradigm integrates the spectral affinity and spatial information into a uniform metric framework. In SS1DME, a spectral-spatial affinity metric is utilized to learn the similarity of HSI pixels. Moreover, a pixel sorted based classification scheme, called 1-Dimensional Manifold Embedding (1DME), which is an extension of smooth ordering, is introduced for objective classification. Four main steps are involved in SS1DME. First, for a high dimensional data set, the proposed paradigm employed the spectral-spatial affinity metric to calculate pixelwise affinity. Next, we embed the whole data set into multiple 1-dimensional manifolds so that connected points have the shortest distance. Then, using the spinning average technique and self-learning scheme, a feasible confident set is constructed from the unlabeled set, where data points in feasible confident set are added to the labeled set in proportion. Finally, we use the extended labeled set to learn the interpolated function, which will lead to classification of unlabeled points. This approach is experimentally superior to some traditional alternatives in terms of classification performance indicators. |
| Starting Page | 394 |
| Ending Page | 398 |
| File Size | 604872 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479983223 |
| DOI | 10.1109/CYBConf.2015.7175966 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-24 |
| Publisher Place | Poland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Manifolds Measurement Support vector machines Interpolation Spectral-spatial information Self-learning Feature extraction Smooth ordering 1-dimensional manifold embedding Pixel sorting Hyperspectral imaging Hyperspectral image classification |
| Content Type | Text |
| Resource Type | Article |
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