Since the accuracy of single feature in scene classification is low, inspired by information fusion, a classification method combined feature-level and decision-level fusion was proposed. Firstly, Scale Invariant Feature Transform-Bag of Words (SIFT-BoW), Gist, Local Binary Patterns (LBP), Laws texture and color histogram features of image were extracted. Then, the classification results of every single feature were fused in the way of Dezert-Smarandache Theory (DSmT) to obtain the decision-level fusion result; at the same time, the five features were serially connected to generate a new feature, the new feature was used to classification to obtain the feature-level fusion result. Finally, the feature-level and decision-level fusion results were adaptively fused to finish classification. To solve the Basic Belief Assignment (BBA) problem of DSmT, a method based on posterior probability matrix was proposed. The accuracy of the proposed method on 21 classes of remote sensing images is 88.61% when training and testing samples are both 50, which is 12.27 percentage points higher than the highest accuracy of single feature. The accuracy of proposed method is also higher than that of the feature-level fusion serial connection or DSmT reasoning decision-level fusion.