A Hierarchical Scheme of Multiple Feature Fusion for High-Resolution Satellite Scene Categorization

Abstract

Scene categorization in high-resolution satellite images has attracted much attention in recent years. However, high intra-class variations, illuminations and occlusions make the task very challenging. In this paper, we propose a classification model based on a hierarchical fusion of multiple features. Highlights of our work are threefold: (1) we use four discriminative image features; (2) we employ support vector machine with histogram intersection kernel (HIK-SVM) and L1-regularization logistic regression classifier (L1R-LRC) in different classification stages, respectively. The soft probabilities of different features obtained by the HIK-SVM are discriminatively fused and fed into the L1R-LRC to obtain the final results; (3) we conduct an extensive evaluation of different configurations, including different feature fusion schemes and different kernel functions. Experimental analysis show that our method leads to state-of-the-art classification performance on the satellite scenes.