High-Resolution Image Classification Using the Dynamic Differential Evolutionary Algorithm Optimized Multi-scale Kernel Support Vector Machine Method

With the fast development of remote sensing techniques, the spatial resolution of remote sensed image are improved significantly. However, the excessive spatial resolution leads to a sharp increase in data volume and spectral information confusion of objects. The multi-scale kernel learning (MSKL) method has shown an excellent advantage in classification of high-resolution satellite image. Nevertheless, the performance of the MSKL is dramatically influenced by the widths and weights of the Radial Basis Function (RBF) kernel, since its multi-scale kernel function is constructed by several RBF kernels. In order to achieve efficient multi-scale classifier, a new dynamic differential evolution (DE) algorithm is introduced in this paper. In addition, the spectral features and spatial fractal texture features of images are synthetically employed to construct the multi-scale kernel. The experimental results show that the multi-scale kernel based on the dynamic DE algorithm is superior to the traditional multi-scale kernel in obtaining a better multi-scale kernel classifier and with higher classification accuracy.