Learning Set Representations for LWIR In-Scene Atmospheric Compensation

Learning Set Representations for LWIR In-Scene Atmospheric Compensation

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivit...

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Bibliographic Details
Main Authors: Nicholas Westing, Kevin C. Gross, Brett J. Borghetti, Jacob Martin, Joseph Meola
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Atmospheric compensation
dimension reduction
hyperspectral imagery
neural networks
Online Access:https://ieeexplore.ieee.org/document/9055124/

Internet

https://ieeexplore.ieee.org/document/9055124/

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