Very high-resolution (VHR) optical satellite imagery offers significant potential for detailed land cover mapping. However, terrain shadows, which appear dark and lack texture and detail, are especially acute at low solar elevations. These shadows hinder the creation of spatially complete and accurate land cover maps, particularly in rugged mountainous environments. While many methods have been proposed to mitigate terrain shadows in remote sensing, they either perform insufficient shadow reduction or rely on high-resolution digital elevation models which are often unavailable for VHR image shadow mitigation. In this paper, we propose a bi-temporal image fusion approach to mitigate terrain shadows in VHR satellite imagery. Our approach fuses a WorldView-2 multispectral image, which contains significant terrain shadows, with a corresponding geometrically registered WorldView-1 panchromatic image, which has minimal shadows. This fusion is applied to improve the mapping of evergreen conifers in temperate mixed mountain forests. To evaluate the effectiveness of our approach, we first improve an existing shadow detection method by Silva et al. (2018) to more accurately detect shadows in mountainous, forested landscapes. Next, we propose a quantitative algorithm that differentiates dark and light terrain shadows in VHR satellite imagery based on object visibility in shadowed areas. Finally, we apply a state-of-the-art 3D U-Net deep learning method to detect evergreen conifers. Our study shows that the proposed approach significantly reduces terrain shadows and enhances the detection of evergreen conifers in shaded areas. This is the first time a bi-temporal image fusion approach has been used to mitigate terrain shadow effects for land cover mapping at a very high spatial resolution. This approach can also be applied to other VHR satellite sensors. However, careful image co-registration will be necessary when applying this technique to multi-sensor systems beyond the WorldView constellation, such as Pléiades or SkySat.

中文翻译：

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使用双时相图像融合减轻极高分辨率卫星图像中的地形阴影，以实现准确的常绿针叶树检测


超高分辨率 （VHR） 光学卫星影像为详细的土地覆盖测绘提供了巨大的潜力。但是，地形阴影看起来很暗，缺乏纹理和细节，在太阳高程较低时尤其严重。这些阴影阻碍了空间完整且准确的土地覆被地图的创建，尤其是在崎岖的山区环境中。虽然已经提出了许多方法来减轻遥感中的地形阴影，但它们要么减少阴影不足，要么依赖于高分辨率数字高程模型，而这些模型通常无法用于 VHR 图像阴影缓解。在本文中，我们提出了一种双时态图像融合方法来减轻 VHR 卫星图像中的地形阴影。我们的方法将包含大量地形阴影的 WorldView-2 多光谱图像与相应的几何配准 WorldView-1 全色图像（阴影最小）融合在一起。这种融合用于改进温带混交山林中常绿针叶树的制图。为了评估我们方法的有效性，我们首先改进了 Silva 等人 （2018） 现有的阴影检测方法，以更准确地检测山区森林景观中的阴影。接下来，我们提出了一种定量算法，该算法根据阴影区域中的物体可见性来区分 VHR 卫星图像中的暗暗地形阴影。最后，我们应用最先进的 3D U-Net 深度学习方法来检测常绿针叶树。我们的研究表明，所提出的方法显着减少了地形阴影并增强了对阴影区域中常绿针叶树的检测。 这是首次使用双时相图像融合方法在非常高的空间分辨率下减轻土地覆盖制图的地形阴影效应。这种方法也可以应用于其他 VHR 卫星传感器。但是，在将此技术应用于 WorldView 星座以外的多传感器系统（如 Pléiades 或 SkySat）时，需要仔细进行图像协同配准。