Multiple Instance Learning Via Embedded Instance Selection（MILES） has shown good performance in dealing with noisy training samples

but its bag prediction rule may introduce new uncertainty into the remote sensing image classification results.In order to overcome this limitation

two popular ensemble learning strategies

Bagging and AdaBoost are integrated with MILES.Two methods are proposed to constrain the uncertainty in remote sensing image classification:re-classification of coarse bags

and integration of MILES with diverse density and maximum likelihood classifier.The experimental results show that the uncertainty of remote sensing image classification can be obviously reduced by the integration of multiple instance learning with ensemble learning.