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A dual band algorithm for shallow water depth retrieval from high spatial resolution imagery with no ground truth

机译:没有地面真理的高空间分辨率图像浅水深度检索的双频带算法

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For shallow water depth retrieval from high spatial resolution satellite images, although numerous empirical models have been developed, it remains impossible to estimate shallow water depths without collection of required ground truth depth. To address this limitation, a new physically based dual band algorithm is developed to estimate shallow water depths using blue and green bands from high spatial resolution multispectral image with no ground truth. The dual band log-linear model is first analytically formulated, which then is used for shallow water depths retrieval by solving all unknown model parameters based on different types of sampling pixels directly extracted from the multispectral image. The adjacent pixel pairs from the intersecting edges of different bottom types across various depths over shallow water area, are employed to calculate the optimal band rotation coefficient unit vector by minimization method. On the basis, the bottom parameter is estimated through the pixels from the coastline. Additionally, the pixels from various depths of same bottom type are also employed to achieve the blue to green band ratio of diffused attenuation coefficient. The sum of the diffuse attenuation coefficients of green band for upwelling and downwelling light is estimated by QAA and Kd algorithms. To evaluate the performance of the proposed algorithm, the GeoEye-1 image covered Jinqing Island and the Chinese Gaofen-2 image across Kaneohe Bay are chosen to achieve shallow water depth by using the proposed algorithm after geo-rectification and atmospheric correction. The validations using the actual water depths show the overall root mean square errors (RMSEs) for the derived water depths are 1.18 m for Jinqing Island and 1.34 m for Kaneohe Bay respectively. Compared to the Lyzenga empirical model, the developed approach can generally achieve slightly better results for shallow water depths with no ground truth data. Finally, the effects of the variation in the model parameters to water depth retrieval are discussed and analyzed.
机译:对于高空间分辨率卫星图像的浅水深度检索,尽管已经开发了许多经验模型,但仍然无法收集所需的地面真相深度估计浅水深度。为了解决这个限制,开发了一种新的物理基础的双频算法,以利用来自高空间分辨率多光谱图像的蓝色和绿色频段来估计浅水深度,没有地理。首先分析双频带对线线性模型,然后通过基于直接从多光谱图像直接提取的不同类型的采样像素来解决所有未知的模型参数来检索的浅水深度。从浅水区上各种深度的不同底部类型的相邻像素对,用于通过最小化方法计算最佳频带旋转系数单元向量。在基础上,底部参数通过来自海岸线的像素估计。另外,来自相同底部类型的各种深度的像素也用于实现扩散衰减系数的蓝色到绿色带比。 QAA和KD算法估计了升高和贫寒光的绿色带的漫反射系数的总和。为了评估所提出的算法的性能,通过在整流和大气校正之后使用所提出的算法,选择跨越京群岛的Geoeye-1图像覆盖金青岛和中国高芬-2图像,以实现浅水深度。使用实际水深的验证显示了衍生的水深的整体均方误差(RMSE)为锦青岛为1.18米,分别为甘蔗湾为1.34米。与Lyzenga实证模型相比,开发的方法通常可以为浅水深度达到略微更好的结果,没有实地真理数据。最后,讨论并分析了模型参数变化对水深检索的影响。

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