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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yanfeng Gu Ying Liu Ye Zhang |
| Copyright Year | 2007 |
| Description | Author affiliation: Harbin Inst. of Technol., Harbin (Yanfeng Gu; Ying Liu; Ye Zhang) |
| Abstract | In this paper, a soft classification algorithm based on composite kernels, which incorporate both spectral and spatial information, is proposed for hyperspectral image. Compared with hard classification, soft classification provides more information about the probabilities one pixel belongs to each class. To calculate these probabilities, the proposed algorithm uses Support Vector Machine (SVM), and it successfully converts SVM output values into probabilities, while at the same time integrates spatial and spectral information by composite kernels. To validate the proposed algorithm, experiments are conducted on hyperspectral images with 126 and 186 bands, and experimental results show that soft classification using SVM can yield better results compared with Maximum Likelihood Classifier (MLC),and the introduction of spectral-spatial kernels can greatly improve classification accuracies. |
| Starting Page | 548 |
| Ending Page | 548 |
| File Size | 462248 |
| Page Count | 1 |
| File Format | |
| ISBN | 0769528821 |
| DOI | 10.1109/ICICIC.2007.90 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-09-05 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Hyperspectral sensors Image converters Support vector machine classification Optimization methods Probability Classification algorithms Kernel Pixel Hyperspectral imaging |
| Content Type | Text |
| Resource Type | Article |
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