神经网络训练策略对高分辨率遥感图像场景分类性能影响的评估

Machine learning have been widely used in high-resolution remote sensing image scene classification task. However, the current research mainly focuses on data features and neural network structure, and the effect of neural network training tricks on remote sensing image classification performance is rarely mentioned. Therefore, this paper selects 7 neural network training tricks commonly used in natural image classification for experiments. According to their experimental performance in 3 large remote sensing image data sets and 4 widely used neural network models, neural network training tricks suitable for remote sensing image scene classification are selected. The effect of multiple neural network training tricks on the scene classification performance of remote sensing images was evaluated in detail through ablation experiment. An effective neural network training strategy was obtained by analyzing the overall accuracy, confusion matrix and Kappa coefficient, and the effectiveness of the neural network training strategy on the scene classification performance of remote sensing images was proved. According to the results of the stacking experiment, the combination of 7 training tricks can show good applicability in different network models and data sets.