High spatial and temporal resolution remote sensing data play an important role in monitoring the rapid change of the earth surface. However, there is an irreconcilable contradiction between the spatial and temporal resolutions of the remote sensing image acquired from a same sensor. The spatiotemporal fusion technology for remote sensing data is an effective way to solve the contradiction. In this paper, we will study the spatiotemporal fusion method based on the convolutional neural network, which can fuse the Landsat data with high spatial but low temporal resolution and MODIS data with low spatial but high temporal resolution, and generate time series data with high spatial resolution. In order to improve the accuracy of spatiotemporal fusion, a residual convolution neural network is proposed. MODIS image is used as the input to predict the residual image between MODIS and Landsat, and the sum of the predicted residual image and MODIS data is used as the predicted Landsat-like image. In this paper, the residual network not only increases the depth of the superresolution network but also avoids the problem of vanishing gradient due to the deep network structure. The experimental results show that the prediction accuracy by our method is greater than that of several mainstream methods.

中文翻译：

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基于深度学习的遥感影像时空融合

高时空分辨率的遥感数据在监测地球表面的快速变化中起着重要作用。但是，从同一传感器获取的遥感图像的空间分辨率和时间分辨率之间存在不可调和的矛盾。遥感数据的时空融合技术是解决这一矛盾的有效途径。在本文中，我们将研究基于卷积神经网络的时空融合方法，该方法可以融合具有高空间低时间分辨率的Landsat数据和具有低空间高时间分辨率的MODIS数据，并生成具有高空间时间的时序数据解析度。为了提高时空融合的准确性，提出了一种残差卷积神经网络。使用MODIS图像作为输入来预测MODIS和Landsat之间的残差图像，并将预测的残差图像和MODIS数据之和用作预测的Landsat类图像。本文的残差网络不仅增加了超分辨率网络的深度，而且避免了由于深度网络结构而导致梯度消失的问题。实验结果表明，我们的方法的预测精度高于几种主流方法。