Tiangong remote sensing natural scene intelligent recognition and interpretablity analysis

Kunnan Liu, J. Li, Guofeng Xu, Peng Wang
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Abstract

This paper focuses on the intelligent recognition of images in the Tiangong remote sensing image dataset and its interpretability analysis. In this paper, we classified the aforementioned dataset, retrained the Resnet-18 model on the training set, and then verified the results on the validation set with an accuracy of 97.9%. Furthermore, this paper presented an interpretability analysis of deep learning for intelligent recognition of the Tiangong remote sensing image dataset.
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天宫遥感自然场景智能识别与可解释性分析
本文主要研究了天宫遥感图像数据集图像的智能识别及其可解释性分析。在本文中,我们对上述数据集进行分类,在训练集上重新训练Resnet-18模型,然后在验证集上对结果进行验证,准确率达到97.9%。在此基础上,提出了一种基于深度学习的天宫遥感图像数据集智能识别可解释性分析方法。
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