Algorithm of remote sensing image classification improved by bands selection and hybrid kernel functions
改进的波段选择混合核函数遥感图像分类算法

As the multi-band of remote sensing image is not easy to imaging, its redundancy image information is not suitable for image classification, what's more, the traditional LMBP algorithm has large iteration number and classification imprecise problems. This paper improved the formula of the OIF index number and separability distance, separated to chose the best band combination, and then used the LMBP algorithm refinement of hybrid kernel function to classify. The simulation results show that the improved method can analyze information of the bands more comprehensive and objective, comparing with the traditional algorithm, the network training iterations are significantly reduced, the classification accuracy and Kappa coefficient can be increased by 5% and 6. 625%, the classification of remote sensing image more effectively.