Mitigating terrain shadows in very high-resolution satellite imagery for accurate evergreen conifer detection using bi-temporal image fusion

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引用次数: 0

Abstract

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.
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利用双时相融合技术减少超高分辨率卫星图像中的地形阴影,准确探测常绿针叶林
甚高分辨率(VHR)光学卫星图像为绘制详细的土地覆盖图提供了巨大的潜力。然而,在太阳高度较低的地方,地形阴影尤为明显,这些阴影看起来很暗,缺乏纹理和细节。这些阴影妨碍了绘制空间上完整和准确的土地覆被图,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,但这些方法要么没有充分减少阴影,要么依赖于高分辨率数字高程模型,而这些模型往往无法用于 VHR 图像阴影的减轻。在本文中,我们提出了一种双时相图像融合方法来减轻 VHR 卫星图像中的地形阴影。我们的方法是将包含大量地形阴影的 WorldView-2 多光谱图像与相应的经过几何注册的 WorldView-1 全色图像进行融合,后者的阴影最小。这种融合方法被用于改善温带山地混交林中常绿针叶林的绘图。为了评估我们方法的有效性,我们首先改进了 Silva 等人(2018 年)的现有阴影检测方法,以便更准确地检测山地森林景观中的阴影。接下来,我们提出了一种定量算法,根据阴影区域中物体的可见度来区分 VHR 卫星图像中的明暗地形阴影。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,所提出的方法大大减少了地形阴影,增强了对阴影区域常绿针叶树的检测。这是首次使用双时相图像融合方法来减轻地形阴影效应,以极高的空间分辨率绘制土地覆盖图。这种方法也可用于其他 VHR 卫星传感器。不过,在将这一技术应用于 WorldView 星座以外的多传感器系统(如 Pléiades 或 SkySat)时,有必要进行仔细的图像共同注册。
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
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