Mosaicking of remote sensing images stitches images of different moments or sensors to produce a new image under a uniform geographic coordinate system. In a mosaicking process, the critical enblending operation is divided into color balance, seamline finder, and fusion of overlapping areas, which is still challenging to maintain color consistency and data fidelity. In this article, a new mosaicking framework using spatiotemporal fusion is proposed to solve the enblending issue. Two additional low-resolution reference images are introduced for each mosaicking image. With spatiotemporal fusion methods, all mosaicking images are reconstructed to a uniform time, then the combination of overlapping areas become easy. Furthermore, a new spatiotemporal fusion method is proposed by cascading enhanced deep neural networks to fuse images quickly and effectively. In the validation procedure, the proposed method is compared with eight color harmony methods or tools by mosaicking the red, green, and blue bands of Landsat-8 images with images from the moderate-resolution imaging spectroradiometer as the reference. The digital evaluations and visual comparisons demonstrate that the newly method outweighs majority methods regarding to the radiometric, structural, and spectral fidelity, which proves the feasibility of our new enblending method.

中文翻译：

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将马赛克遥感图像与卷积神经网络的时空融合相融合


遥感图像马赛克将不同时刻或传感器的图像拼接起来，在统一的地理坐标系下产生新的图像。在镶嵌过程中，关键的混合操作分为色彩平衡、接缝线查找和重叠区域的融合，这对于保持色彩一致性和数据保真度仍然具有挑战性。在本文中，提出了一种使用时空融合的新镶嵌框架来解决混合问题。为每个镶嵌图像引入两个附加的低分辨率参考图像。通过时空融合方法，所有镶嵌图像都被重建到统一的时间，然后重叠区域的组合变得容易。此外，还提出了一种新的时空融合方法，通过级联增强型深度神经网络来快速有效地融合图像。在验证过程中，通过以中等分辨率成像光谱辐射计的图像作为参考，镶嵌 Landsat-8 图像的红色、绿色和蓝色波段，将所提出的方法与八种色彩和谐方法或工具进行比较。数字评估和视觉比较表明，新方法在辐射、结构和光谱保真度方面优于大多数方法，这证明了我们新的混合方法的可行性。