Ill-posed Surface Emissivity Retrieval from Multi-Geometry
HyperspectralImages using a Hybrid Deep Neural Network
- URL: http://arxiv.org/abs/2107.04631v1
- Date: Fri, 9 Jul 2021 18:59:58 GMT
- Title: Ill-posed Surface Emissivity Retrieval from Multi-Geometry
HyperspectralImages using a Hybrid Deep Neural Network
- Authors: Fangcao Xu, Jian Suna, Guido Cervonea, Mark Salvador
- Abstract summary: Atmospheric correction is a fundamental task in remote sensing because observations are taken either of the atmosphere or looking through it.
A geometry-dependent hybrid neural network is proposed for automatic atmospheric correction using multi-scan hyperspectral data.
Results show that the proposed network has the capacity to accurately characterize the atmosphere and estimate target emissivity spectra with a Mean Absolute Error (MAE) under 0.02 for 29 different materials.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Atmospheric correction is a fundamental task in remote sensing because
observations are taken either of the atmosphere or looking through the
atmosphere. Atmospheric correction errors can significantly alter the spectral
signature of the observations, and lead to invalid classifications or target
detection. This is even more crucial when working with hyperspectral data,
where a precise measurement of spectral properties is required.
State-of-the-art physics-based atmospheric correction approaches require
extensive prior knowledge about sensor characteristics, collection geometry,
and environmental characteristics of the scene being collected. These
approaches are computationally expensive, prone to inaccuracy due to lack of
sufficient environmental and collection information, and often impossible for
real-time applications. In this paper, a geometry-dependent hybrid neural
network is proposed for automatic atmospheric correction using multi-scan
hyperspectral data collected from different geometries. The proposed network
can characterize the atmosphere without any additional meteorological data. A
grid-search method is also proposed to solve the temperature emissivity
separation problem. Results show that the proposed network has the capacity to
accurately characterize the atmosphere and estimate target emissivity spectra
with a Mean Absolute Error (MAE) under 0.02 for 29 different materials. This
solution can lead to accurate atmospheric correction to improve target
detection for real time applications.
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