Image-derived constraints for inversion of physics-based methods in satellite-derived bathymetry

卫星测深中基于物理方法反演的图像导出约束

• 批准号: 513717-2017
• 负责人: Knudby, Anders
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640188
• 关键词: Image; derived; constraints; inversion; physics

Two groups of methods exist to estimate water depth from satellite imagery: empirical methods are simple toimplement but require in-situ calibration data, while physics-based methods are more complicated but can beimplemented with the need for in-situ data. The latter are thus necessary for mapping areas where no in-situdata exist and none can feasibly be acquired, such as for the remote Arctic or foreign territory. Physics-basedmethods simultaneously derive information on water depth, seafloor reflectance, and water optical quality, on apixel-by-pixel basis, by inverting a forward radiative transfer model parameterized with these quantities.However, when used with multispectral satellite data suffering from imperfect atmospheric correction,inversion algorithms must be robust to spectrally correlated and spatially uncorrelated noise. Robust algorithmsmay rely on the reasonable assumption of locally constant water quality, and on image-based indications of biasfrom atmospheric correction. In this research, we propose to develop and test robust inversion algorithms thatimprove bathymetry retrievals from multispectral satellite data. We will do this by using low-altitude airbornehyperspectral data coincident with satellite overpasses to identify and isolate atmospheric correction biases, andby developing stronger constraints on inversion, for example deriving from assumptions of regionallyhomogeneous water quality ranging from an optically deep area (where the seafloor does not influence thecolour of the area as seen from space) to a nearby optically shallow area targeted for bathymetry mapping. Theresearch will improve bathymetry mapping from multispectral satellite images, increasing the value ofsatellite-derived bathymetry in Canada and beyond.

存在两组从卫星图像估计水深的方法：经验方法易于实施，但需要现场校准数据，而基于物理的方法更复杂，但可以在需要现场数据的情况下实施。因此，后者对于绘制不存在现场数据且无法切实获取任何数据的区域（例如偏远的北极或外国领土）是必要的。基于物理的方法通过反演用这些量参数化的前向辐射传输模型，在逐像素的基础上同时导出有关水深、海底反射率和水光学质量的信息。然而，当与大气校正不完善的多光谱卫星数据一起使用时，反演算法必须对光谱相关和空间不相关的噪声具有鲁棒性。鲁棒算法可能依赖于局部恒定水质的合理假设，以及基于图像的大气校正偏差指示。在这项研究中，我们建议开发和测试稳健的反演算法，以改进多光谱卫星数据的测深检索。我们将通过使用与卫星立交桥一致的低空机载高光谱数据来识别和隔离大气校正偏差，并通过对反演制定更强的约束，例如从光学深层区域（海底不影响从太空看到的区域的颜色）到附近光学浅层区域的区域均匀水质假设得出这一点。 测深测绘。研究将改进多光谱卫星图像的测深测绘，增加加拿大及其他地区卫星测深的价值。