원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.8 No.7
2015.07
pp.17-26
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Through the research of the application of support vector machine theory in the pixel unmixing, the advantages of the weighted posterior probability support vector machine theory in pixel unmixing is presented. Considering the difference of each support vector machine classifier, posterior probability pixel is used as weight coefficient of sub-pixel classification for pixels unmixing. This paper presents a weighted posterior probability support vector machine mixed pixel unmixing method. This method not only has the nonlinear model decomposition characteristics of high precision, but also reduces the standard support vector machine calculating the amount of multi classifier, and it has strong adaptability.
목차
Abstract
1. Introduction
2. The Principle of Support Vector machine
3. Application of Support Vector Machine in the Processing of Remote Sensing Image
4. Support Vector Machine based on Weighted Posteriori Probability
4.1. Support Vector Machine based on Weighted Posteriori Probability
4.2. Method for Determining the Weighted Posterior Probability of Sample Points
5. Experimental Analysis and Results
5.1. Experimental Data
5.2. Accuracy Evaluation
6. Conclusion
Acknowledgement
References
1. Introduction
2. The Principle of Support Vector machine
3. Application of Support Vector Machine in the Processing of Remote Sensing Image
4. Support Vector Machine based on Weighted Posteriori Probability
4.1. Support Vector Machine based on Weighted Posteriori Probability
4.2. Method for Determining the Weighted Posterior Probability of Sample Points
5. Experimental Analysis and Results
5.1. Experimental Data
5.2. Accuracy Evaluation
6. Conclusion
Acknowledgement
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보