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

Translated title of the contribution: Evaluation of the Effect of Neural Network Training Tricks on the Performance of High-Resolution Remote Sensing Image Scene Classification

Hai Ying Zheng, Feng Wang, Wei Jiang, Zhi Qiang Wang, Xi Wen Yao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 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.

Translated title of the contributionEvaluation of the Effect of Neural Network Training Tricks on the Performance of High-Resolution Remote Sensing Image Scene Classification
Original languageChinese (Traditional)
Pages (from-to)1599-1614
Number of pages16
JournalTien Tzu Hsueh Pao/Acta Electronica Sinica
Volume49
Issue number8
DOIs
StatePublished - Aug 2021

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