We present a framework for inferring an atmospheric transmission profile from
a spectral scene. This framework leverages a lightweight, physics-based
simulator that is automatically tuned - by virtue of autodifferentiation and
differentiable programming - to construct a surrogate atmospheric profile to
model the observed data. We demonstrate utility of the methodology by (i)
performing atmospheric correction, (ii) recasting spectral data between various
modalities (e.g. radiance and reflectance at the surface and at the sensor),
and (iii) inferring atmospheric transmission profiles, such as absorbing bands
and their relative magnitudes.

我们提出了一种从光谱场景中推断大气传输剖面的框架。该框架利用一个轻量级、基于物理的模拟器，通过自动微分和可微编程来构建一个替代的大气剖面模型，以模拟观测数据。我们通过（i）进行大气校正，（ii）重新构建不同模态间的光谱数据（例如地表和传感器的辐射和反射率），以及（iii）推断大气传输剖面，如吸收带和它们的相对强度，展示了该方法的实用性。

基于数据驱动的大气透射反转神经代理

Data-Driven Invertible Neural Surrogates of Atmospheric Transmission

Voronoi tessellation, also known as Voronoi diagram, is an important
computational geometry technique that has applications in various scientific
disciplines. It involves dividing a given space into regions based on the
proximity to a set of points. Autodifferentiation is a powerful tool for
solving optimization tasks. Autodifferentiation assumes constructing a
computational graph that allows to compute gradients using backpropagation
algorithm. However, often the Voronoi tessellation remains the only
non-differentiable part of a pipeline, prohibiting end-to-end differentiation.
We present the method for autodifferentiation of the 2D Voronoi tessellation.
The method allows one to construct the Voronoi tessellation and pass gradients,
making the construction end-to-end differentiable. We provide the
implementation details and present several important applications. To the best
of our knowledge this is the first autodifferentiable realization of the
Voronoi tessellation providing full set of Voronoi geometrical parameters in a
differentiable way.

在本文中，我们介绍了自动微分的一种方法来构建二维 Voronoi 图，并实现了端到端的微分，从而解决了在优化任务中 Voronoi 图的不可微分性问题，同时展示了其在几个重要应用中的实现细节和优势。