Remote Sensing Image Scene Classification Based on SURF Feature and Deep Learning

Abstract

Remote sensing image scene classification is one of the key points in remote sensing image interpretation. The traditional remote sensing image scene classification feature performance is not strong, and the deep learning extraction semantic feature process is complex. This paper proposes a fusion feature remote sensing image scene classification method which is based on artificial features and deep learning semantic features. Firstly, the SURF feature of the remote sensing image is extracted and encoded by the VLAD algorithm. The semantic feature of a remote sensing image is extracted by transfer learning. Then the feature reduction is performed by PCA algorithm and feature fusion is performed. Finally, the scene classifier is trained by using the random forest algorithm. The experimental results show that the classification accuracy and Kappa coefficient of this method are higher and the method is effective.