| Liu Xiaofang（刘小芳）②* **,He Binbin*,Li Xiaowen*.[J].高技术通讯(英文),2011,17(4):427~432 |
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| Semi-supervised kernel FCM algorithm for remote sensing image classification① |
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| DOI： |
| 中文关键词: |
| 英文关键词: remote sensing image classification, semi-supervised kernel fuzzy C-means (SSKFCM) algorithm, Beijing-1 micro-satellite, semi-supervised learning technique, kernel method |
| 基金项目: |
| Author Name | Affiliation | | Liu Xiaofang（刘小芳）②* ** | | | He Binbin* | | | Li Xiaowen* | |
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| 中文摘要: |
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| 英文摘要: |
| These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means (SSKFCM) algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means (FCM) algorithm. A set of Beijing-1 micro-satellite’s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM (KFCM), semi-supervised FCM (SSFCM) and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others. |
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