由于高质量的对地观测需要时空连续的高分辨率遥感图像,故对时空融合的研究广泛开展,并且集中在Landsat和MODIS卫星之间。目前已经提出了使用卷积神经网络进行时空融合的方法,但是网络较浅,故融合性能有限。针对应用最广泛的单对图像时空融合问题,建立了一种基于深度神经网络的新时空融合方法。首先,基本网络框架由两个级联的4倍上采样器构成以近似Landsat和MODIS卫星之间的空间差异和传感器差异。然后,利用卷积神经网络学习重建图像与真实图像之间的残差,使重建图像与真实图像更接近。接着,使用高通调制策略进行时间上的预测。最后,将所提方法在不同的Landsat和MODIS卫星图像上进行了测试,并与多种时空融合算法进行了比较。实验结果表明,与现有融合算法相比,所提方法的重建效果更好,且处理速度更快。

Due to high-quality earth observation requires spatiotemporal continuous high-resolution remote sensing images, the research on spatiotemporal fusion is widely carried out and focused on Landsat and MODIS satellites. At present, the method of spatiotemporal fusion using convolutional neural networks has been proposed, but the network is shallow, so the fusion performance is limited. Aiming at the most widely used one-pair image spatiotemporal fusion, a new spatiotemporal fusion method based on deep neural network is established. Firstly, the basic network framework consists of two cascaded upsamplers with quadruple magnification to approximate the spatial difference and sensor difference between Landsat and MODIS satellites. Then, the residual error between the reconstructed image and the real image is learned by the convolutional neural network to make the reconstructed image closer to the real image. Moreover,the time prediction is carried out by highpass moduation strategy. Finally, the proposed method is tested on different Landsat and MODIS satellite images and compared with many spatiotemporal fusion algorithms. The experimental results show that, compared with the existing fusion algorithms, the reconstruction effect of the proposed method is better and the processing speed is faster.