Remote Sensing Image Classification with Multiple Classifiers based on Support Vector Machines
Classification accuracy is one of major factors influencing the application of classified image. This Paper proposes a SVM-based multiple classifiers fusion method for remote sensing image classification. We use both spatial Gabor wavelet texture feature and spectral feature to construct SVM classifier separately. Then taking advantage of characteristic of SVM, namely for a given sample, the larger is the distance to the hyperplane, the more reliable is the class label. So the most reliable classification result is thus the one that gives the largest distance. This is our decision fusion rule. Using Landsat ETM+ satellite image as test data, the experimental results indicate that all classes including water, mountain, gobi, vegetation, desert and resident area could be well classified, and the overall accuracy achieved 86.5％, more than other each separate SVM classifier.
remote sensing image SVM classification multiple classifiers
Wei WU Guanglai GAO
Computer Science Department, Inner Mongolia University, Huhhot, China
国际会议
杭州
英文
188-191
2012-10-28（万方平台首次上网日期，不代表论文的发表时间）