A Novel Feature Fusion Approach for VHR Remote Sensing Image Classification

This article develops a robust feature fusion approach to enhance the classification performance of very high resolution (VHR) remote sensing images. Specifically, a novel two-stage multiple feature fusion (TsF) approach is proposed, which includes an intragroup and an intergroup feature fusion stages. In the first fusion stage, multiple features are grouped by clustering, where redundant information between different types of features is eliminated within each group. Then, features are pairwisely fused in an intergroup fusion model based on the guided filtering method. Finally, the fused feature set is imported into a classifier to generate the classification map. In this work, the original VHR spectral bands and their attribute profiles are taken as examples as input spectral and spatial features, respectively, in order to test the performance of the proposed TsF approach. Experimental results obtained on two QuickBird datasets covering complex urban scenarios demonstrate the effectiveness of the proposed approach in terms of generation of more discriminative fusion features and enhancing classification performance. More importantly, the fused feature dimensionality is limited at a certain level; thus, the computational cost will not be significantly increased even if multiple features are considered.