Name
Abstract | ||
|---|---|---|
Classification is one of the most important procedures in high-resolution remotely sensed image information extraction. This paper introduced Adaboost-SVM algorithm to IKONOS image classification. The classification performance of Adabost-SVM and single SVM were quantitatively analyzed and qualitatively evaluated. The results show that: In the case of small training samples, Adaboost-SVM outperforms single SVM in terms of classification accuracy greatly, and the training time of it is not too long. At the same time it can deal with the classes which are difficult for a single SVM to identify. In the case of big training samples, the generalization of Adaboost-SVM and single SVM are basically the same, but the training time of Adaboost-SVM is unbearable. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/GEOINFORMATICS.2010.5568055 | Geoinformatics |
Keywords | Field | DocType |
geophysical techniques,adaboost-svm algorithm,classification,training samples,adaboost,learning (artificial intelligence),ikonos image classification,svm,image classification,support vector machine,ikonos,geophysical image processing,eastern china,remotely sensed image information extraction,nanjing city,classification accuracy,support vector machines,training time,accuracy,information extraction,testing,kernel,high resolution,learning artificial intelligence,classification algorithms | Kernel (linear algebra),AdaBoost,Pattern recognition,Computer science,Support vector machine,Information extraction,Artificial intelligence,Statistical classification,Contextual image classification,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-7301-4 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengming Liu | 1 | 3 | 2.14 |
| Manchun Li | 2 | 211 | 45.40 |
| Yongxue Liu | 3 | 40 | 8.38 |
| Jieli Chen | 4 | 1 | 1.74 |
| Chenglei Shen | 5 | 1 | 1.74 |