Extreme value theory-based calibration for the fusion of multiple features in high-resolution satellite scene classification

This article presents a hierarchical classification method for high-resolution satellite imagery incorporating extreme value theory (EVT)-based normalization to calibrate multiple-feature scores. First, a simple linear iterative clustering algorithm is used to over-segment an image to build a superpixel representation of the scene. Then, each superpixel is characterized by three different visual descriptors. Finally, a two-phase classification model is proposed for achieving classification of the scene: (1) in the first phase, a support vector machine (SVM) with histogram intersection kernel is applied to each feature channel to obtain raw soft probability; and (2) in the second phase, the derived soft outputs are multiplied to build a product space for score-level fusion. The fused scores are subsequently further calibrated using the EVT and fed to an L1-regularized L2-loss SVM to obtain the final result. Experimental analysis on high-resolution satellite scenes shows that the proposed method achieves promising classification results and outperforms other competitive methods.