STUDY ON THE EFFECT OF TRAINING SAMPLE ON CLASSIFICATION ACCURACY OF REMOTE SENSING IMAGE
SPOT- 5 HRGimage and Landsat TMimage as a data source in Dali,on the basis of building standards on the training sample data set to explore the impact of training samples to remote sensing image classification. Selecting the different combinations of training samples, respectively, supervised the six classification of parallelepiped,minimum distance, Mahalanobis distance, maximum likelihood, neural networks and support vector machine( SVM). Using Kappa coefficient and the overall classification precision of the experimental accuracy of the classification results of the evaluation. The experimental results showthat the outcome should be the average of multiple classification as the final classification results can reduce the random error; increased training samples can reduce the number of samples of random errors caused by single classification; SVMis the highest classification accuracy under the different training samples.