Semi-Supervised Classification based on Gaussian Mixture Model for remote imagery
β Scribed by Biao Xiong; XiaoJun Zhang; WanShou Jiang
- Publisher
- SP Science China Press
- Year
- 2010
- Tongue
- English
- Weight
- 458 KB
- Volume
- 53
- Category
- Article
- ISSN
- 1006-9321
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Textural features play increasingly an important role in remotely sensed images classification and many pattern recognition applications. However, the selection of informative ones with highly discriminatory ability to improve the classification accuracy is still one of the well-known problems in re
Rolling bearings are common and vital elements in rotating machinery and vibration signal is a kind of effective mean to characterize the status of rolling bearing fault and its severity. In this paper, a novel method is introduced to realize classification of fault signal without extracting feature