Bathymetric map of Heron Reef, Australia, derived from airborne hyperspectral data at 1 m resolution

A simple method for efficient inversion of arbitrary radiative transfer models for image analysis is presented. The method operates by representing the shape of the function that maps model parameters to spectral reflectance by an adaptive look-up tree (ALUT) that evenly distributes the discretization error of tabulated reflectances in spectral space. A post-processing step organizes the data into a binary space partitioning tree that facilitates an efficient inversion search algorithm. In an example shallow water remote sensing application, the method performs faster than an implementation of previously published methodology and has the same accuracy in bathymetric retrievals. The method has no user configuration parameters requiring expert knowledge and minimizes the number of forward model runs required, making it highly suitable for routine operational implementation of image analysis methods. For the research community, straightforward and robust inversion allows research to focus on improving the radiative transfer models themselves without the added complication of devising an inversion strategy.

本文提出一种面向图像分析的任意辐射传输模型高效反演方法。该方法通过自适应查找树（adaptive look-up tree, ALUT）表征将模型参数映射至光谱反射率的函数形态，该树可将制表反射率的离散化误差均匀分布于光谱空间中。后续通过后处理步骤将数据组织为二叉空间分割树，以此支撑高效的反演搜索算法。在浅水遥感应用示例中，该方法的运行速度快于已发表的同类方法实现方案，且在水深反演精度上保持一致。该方法无需用户配置需要专业知识的参数，同时最小化了所需的正向模型运行次数，因此非常适用于图像分析方法的常规业务化部署。对于科研界而言，该方法具备简洁且鲁棒的反演能力，使得研究人员可专注于辐射传输模型本身的改进，无需额外承担设计反演策略的复杂工作。