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Explicit and stepwise models for spatiotemporal fusion of remote sensing images with deep neural networks
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2021-11-03 , DOI: 10.1016/j.jag.2021.102611
Yaobin Ma 1, 2 , Jingbo Wei 3 , Wenchao Tang 3 , Rongxin Tang 2, 3
Affiliation  

The spatial, sensor, and temporal differences can be observed in the process of spatiotemporal fusion because source images are from different sensors or moments. The existing spatiotemporal fusion methods have modelled the temporal difference, but they did not solve the spatial difference or the sensor difference to build complete models. In this paper, a step-by-step modelling framework is proposed, and three models are designed based on deep neural networks to model the spatial difference, sensor difference, and temporal difference in a separate and explicit way. The spatial difference is modelled with cascaded dual regression networks. The sensor difference is simulated with a four-layer convolutional neural network. The temporal difference is predicted with a generative adversarial network. The proposed method is compared with six algorithms for the reconstruction of Landsat-7 and Landsat-5 which validates the effectiveness of the spatial fusion strategy. The digital evaluation on radiometric, structural, and spectral loss illustrates that the proposed method can give the optimal performance steadily. The necessity of complete modelling is also tested by connecting the spatial and sensor models of the proposed method with one-pair fusion methods, and the steadily improved performance shows that all the difference models contribute to performance improvement.



中文翻译:

遥感图像与深度神经网络时空融合的显式和逐步模型

由于源图像来自不同的传感器或时刻,因此在时空融合过程中可以观察到空间、传感器和时间的差异。现有的时空融合方法对时间差异进行了建模,但没有解决空间差异或传感器差异来构建完整的模型。在本文中,提出了一个循序渐进的建模框架,基于深度神经网络设计了三个模型,以单独和显式的方式对空间差异、传感器差异和时间差异进行建模。空间差异用级联对偶回归网络建模。传感器差异用四层卷积神经网络模拟。时间差异是用生成对抗网络预测的。将所提出的方法与六种算法对Landsat-7和Landsat-5的重建进行比较,验证了空间融合策略的有效性。对辐射、结构和光谱损失的数字评估表明,所提出的方法可以稳定地给出最佳性能。还通过将所提出方法的空间模型和传感器模型与一对融合方法相连接来测试完整建模的必要性,性能的稳步提高表明所有差异模型都有助于性能提高。

更新日期:2021-11-03
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