Invertible Neural Surrogates for Atmospheric Correction of Hyperspectral Imagery

Concetti Chiave

A framework for constructing lightweight, physics-based neural surrogates to model atmospheric transmission and enable accurate atmospheric correction of hyperspectral imagery.

Sintesi

The content presents a novel framework called DINSAT (Data-Driven Invertible Neural Surrogates of Atmospheric Transmission) for performing atmospheric correction on hyperspectral imagery. The key highlights are:

DINSAT leverages the concept of Neural Ordinary Differential Equations (Neural ODEs) to construct tunable, physics-based models of atmospheric transmission. This allows the framework to be both invertible and physically consistent.

The authors demonstrate DINSAT in two settings: supervised learning with known ground-truth reflectance data, and unsupervised learning using only at-sensor radiance measurements. In both cases, DINSAT is able to accurately estimate surface reflectance and reconstruct at-sensor radiance.

Compared to standard atmospheric correction methods like QUAC and FLAASH, DINSAT requires less metadata (e.g. lighting, geometry, temperature) and can generalize better to out-of-distribution data.

The framework is designed to be extensible, allowing for the inclusion of more complex physical effects like adjacency, stochastic processes, and time-varying atmospheric profiles.

Overall, DINSAT provides a flexible and powerful approach for performing atmospheric correction on hyperspectral imagery, with potential applications in remote sensing, Earth observation, and other domains involving radiative transfer modeling.

Statistiche

The content does not provide specific numerical data or metrics, but rather focuses on the high-level methodology and demonstration of the DINSAT framework.

Citazioni

"DINSAT is a modeling framework for inferring atmospheric transmission profiles suitable for atmospheric correction and transmission modeling tasks."
"We seek to construct minimally-parameterized models for atmospheric correction that are (i) invertible, (ii) physically consistent, (iii) robust to out-of-distribution data, and (iv) have minimal data (at-sensor radiance) and meta-data (lighting, geometry, temperature, etc.) requirements for training."
"The ODESolve(·) can be made differentiable through backpropagating through the elementary operations of the solver (e.g. differentiable programming) or via an adjoint state method [1]."

Approfondimenti chiave tratti da

Data-Driven Invertible Neural Surrogates of Atmospheric Transmission

by James Koch,B... alle arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19605.pdf

Domande più approfondite

How can the DINSAT framework be extended to handle more complex atmospheric effects, such as aerosol scattering and temporal variations?

The DINSAT framework can be extended to handle more complex atmospheric effects by incorporating additional physics-based models and data sources. For aerosol scattering, the framework could integrate models that simulate the interaction of electromagnetic radiation with aerosols in the atmosphere. This would involve modifying the transmission operator to account for the scattering properties of aerosols, potentially through additional terms in the differential equations governing the transmission profile.
To address temporal variations, the framework could be enhanced to include time-dependent parameters in the transmission models. By introducing time-varying components in the differential equations, DINSAT could capture changes in atmospheric conditions over different time scales. This would enable the framework to adapt to dynamic atmospheric phenomena, such as diurnal variations in atmospheric properties.
Furthermore, incorporating real-time data sources, such as weather forecasts or satellite observations, could provide valuable information for modeling temporal variations. By updating the transmission profiles based on current atmospheric conditions, DINSAT could improve its accuracy in correcting for time-dependent atmospheric effects.

What are the potential limitations of the current DINSAT approach, and how could it be further improved to enhance its robustness and generalization capabilities?

One potential limitation of the current DINSAT approach is its reliance on simplified physics-based models, which may not fully capture the complexity of atmospheric interactions. To address this limitation, the framework could benefit from incorporating more sophisticated radiative transfer models that account for multiple scattering processes, polarization effects, and wavelength-dependent phenomena.
Moreover, the generalization capabilities of DINSAT could be enhanced by introducing regularization techniques to prevent overfitting and improve model robustness. Techniques such as dropout regularization, data augmentation, and ensemble learning could help mitigate the risk of model memorization and improve performance on unseen data.
Additionally, incorporating uncertainty quantification methods, such as Bayesian neural networks or Monte Carlo dropout, could provide valuable insights into the reliability of the model predictions. By estimating the uncertainty associated with atmospheric correction results, DINSAT could offer more reliable and interpretable outputs, especially in challenging or novel scenarios.

Given the flexibility of the DINSAT framework, how could it be adapted to address other remote sensing challenges beyond atmospheric correction, such as scene understanding or target detection?

The flexibility of the DINSAT framework allows for its adaptation to various remote sensing challenges beyond atmospheric correction. For scene understanding, DINSAT could be extended to incorporate semantic segmentation techniques that classify different land cover types or objects within a spectral scene. By training the framework on annotated datasets with scene labels, DINSAT could learn to identify and categorize different features in the imagery.
In the context of target detection, DINSAT could be leveraged to develop anomaly detection algorithms that identify unusual or suspicious objects in hyperspectral images. By training the framework on normal spectral signatures and detecting deviations from the expected patterns, DINSAT could assist in target detection tasks, such as identifying hidden objects or monitoring changes in the environment.
Furthermore, DINSAT could be adapted for change detection applications by comparing spectral data acquired at different time points. By analyzing differences in the atmospheric transmission profiles between temporal snapshots, the framework could highlight areas of significant change, such as land cover transformations or environmental disturbances. This adaptability showcases the potential of DINSAT to address a wide range of remote sensing challenges beyond traditional atmospheric correction.