Inferring river bathymetry via Image‐to‐Depth Quantile Transformation (IDQT)
Inferring river bathymetry via Image‐to‐Depth Quantile Transformation (IDQT)
复制标题
通过图像深度分位数变换 (IDQT) 推断河流水深
DOI：
10.1002/2016wr018730
复制
发表时间：
2016
影响因子：
5.4
通讯作者：
C. Legleiter
中科院分区：
文献类型：
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作者：
C. Legleiter
传统的、基于回归的从被动光学图像数据推断深度的方法削弱了遥感在表征河流系统方面的优势。本研究介绍并评估了一个更灵活的框架，图像到深度的分位数变换（IDQT），它涉及将像素值的频率分布与深度的频率分布联系起来。此外，一种新的涉及深水校正和最小噪声分数（MNF）变换的图像处理工作流程可以将高光谱数据集减少到与深度相关的单个变量，从而适合输入到IDQT。应用于砾石河床，IDQT避免了沿河道边缘的负深度估计和对水池深度的低估。深度检索精度（R2 = 0.79）和精度（0.27 m）与基于频带比的方法相当，尽管观察到较小的浅层偏差（0.04 m）。我们评估了几种指定像素值和深度分布的方法，但它们对最终深度估计的影响可以忽略不计，这意味着IDQT对这些实现细节是健壮的。本质上，IDQT使用像素值和深度的频率分布来实现空间校准；图像本身提供了深度的空间分布信息。因此，该方法降低了对现场和图像数据集之间不一致的敏感性，并且相对于图像采集，在现场数据收集的时间方面具有更大的灵活性，这在动态通道中是一个显著的优势。IDQT还为在没有现场数据的情况下进行深度检索创造了新的可能性，如果可以使用一个模型来预测可达范围内的深度分布。
Conventional, regression‐based methods of inferring depth from passive optical image data undermine the advantages of remote sensing for characterizing river systems. This study introduces and evaluates a more flexible framework, Image‐to‐Depth Quantile Transformation (IDQT), that involves linking the frequency distribution of pixel values to that of depth. In addition, a new image processing workflow involving deep water correction and Minimum Noise Fraction (MNF) transformation can reduce a hyperspectral data set to a single variable related to depth and thus suitable for input to IDQT. Applied to a gravel bed river, IDQT avoided negative depth estimates along channel margins and underpredictions of pool depth. Depth retrieval accuracy (R2 = 0.79) and precision (0.27 m) were comparable to an established band ratio‐based method, although a small shallow bias (0.04 m) was observed. Several ways of specifying distributions of pixel values and depths were evaluated but had negligible impact on the resulting depth estimates, implying that IDQT was robust to these implementation details. In essence, IDQT uses frequency distributions of pixel values and depths to achieve an aspatial calibration; the image itself provides information on the spatial distribution of depths. The approach thus reduces sensitivity to misalignment between field and image data sets and allows greater flexibility in the timing of field data collection relative to image acquisition, a significant advantage in dynamic channels. IDQT also creates new possibilities for depth retrieval in the absence of field data if a model could be used to predict the distribution of depths within a reach.
影响因子：
3.3
作者：
R. Williams;J. Brasington;D. Vericat;D. Hicks
通讯作者：
R. Williams;J. Brasington;D. Vericat;D. Hicks