Deep Learning of Radiative Atmospheric Transfer with an Autoencoder

TL;DR

This paper introduces an autoencoder-based deep learning approach to separate atmospheric effects from ground reflectance in hyperspectral imagery, aiming to improve atmospheric compensation in remote sensing.

Contribution

It presents a novel autoencoder model trained with physics-based simulated data to distinguish atmospheric effects from ground signals in hyperspectral data.

Findings

Autoencoder can separate atmospheric effects from ground reflectance.

Generated large synthetic dataset using MODTRAN for training.

Initial results show promise despite lower accuracy than existing methods.

Abstract

As electro-optical energy from the sun propagates through the atmosphere it is affected by radiative transfer effects including absorption, emission, and scattering. Modeling these affects is essential for scientific remote sensing measurements of the earth and atmosphere. For example, hyperspectral imagery is a form of digital imagery collected with many, often hundreds, of wavelengths of light in pixel. The amount of light measured at the sensor is the result of emitted sunlight, atmospheric radiative transfer, and the reflectance off the materials on the ground, all of which vary per wavelength resulting from multiple physical phenomena. Therefore measurements of the ground spectra or atmospheric constituents requires separating these different contributions per wavelength. In this paper, we create an autoencoder similar to denoising autoencoders treating the atmospheric affects as 'noise' and ground reflectance as truth per spectrum. We generate hundreds of thousands of training samples by taking random samples of spectra from laboratory measurements and adding atmospheric affects using physics-based modelling via MODTRAN (http://modtran.spectral.com/modtran\_home) by varying atmospheric inputs. This process ideally could create an autoencoder that would separate atmospheric effects and ground reflectance in hyperspectral imagery, a process called atmospheric compensation which is difficult and time-consuming requiring a combination of heuristic approximations, estimates of physical quantities, and physical modelling. While the accuracy of our method is not as good as other methods in the field, this an important first step in applying the growing field of deep learning of physical principles to atmospheric compensation in hyperspectral imagery and remote sensing.