【摘要】 机器学习方法在高分辨率遥感图像场景分类任务中已经得到大规模应用,但当前研究主要围绕数据特征和神经网络结构展开,极少提及神经网络训练策略对遥感图像分类性能的影响.因此,本文选取7种自然图像分类中常用的神经网络训练策略进行实验,根据其在3个规模较大的遥感图像数据集和4个广泛使用的神经网络模型上的实验表现,筛选出适用于遥感图像场景分类的神经网络训练策略.通过消融研究详细评估多个神经网络训练策略对遥感图像场景分类性能的影响,通过分析总体分类精度、混淆矩阵、Kappa系数得到有效的神经网络训练策略,并证明神经网络训练策略对遥感图像场景分类性能的有效性;根据叠加实验的结果分析,7种训练策略的组合可以在不同网络模型和数据集上表现出良好的适用性.更多还原

【Abstract】 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 selects7 neural network training tricks commonly used in natural image classification for experiments. According to their experimental performance in3 large remote sensing image data sets and4 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 of7 training tricks can show good applicability in different network models and data sets.更多还原