The UAV Image Classification Method Based on the Grey-Sigmoid Kernel Function Support Vector Machine

摘要

Since SVM is sensitive to the noises and outliers in the training set, a new SVM algorithm based on affinity Grey-Sigmoid kernel is proposed in the paper. The cluster membership is defined by the distance from the cluster center, but also defined by the affinity among samples. The affinity among samples is measured by the minimum super sphere which containing the maximum of the samples. Then the Grey degree of samples are defined by their position in the super sphere. Compared with the SVM based on traditional Sigmoid kernel, experimental results show that the Grey-Sigmoid kernel is more robust and efficient.