Deep Learning for High-dimensional Parameter Retrieval. Malmgren-Hansen, D. In Deep learning for the Earth Sciences, pages 240–257. John Wiley & Sons, Ltd, 2021. Section: 16 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119646181.ch16![Deep Learning for High-dimensional Parameter Retrieval [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Modeling the state of the Earth in biology, ecology, climate, or geophysics, require describing it with a set of parameters which we can frame as bio-geophysical parameters. Retrieving these from satellites provides dense and frequent coverage and this task is an important area of Remote Sensing that supports the broader field of Earth Sciences. Parameter retrieval from satellite measurements is an area deep learning (DL) has started having a growing impact on, and this is likely to increase in the future. Despite this, several challenges need to be addressed for DL to solve and improve the current and future retrieval problems. This chapter outlines some of these challenges, which include the computational load of high-dimensional problems and defining the learning objectives for each problem at hand. Examples from two cases of bio-geophysical parameter retrieval which highlight these challenges are provided.
@incollection{malmgren-hansen_deep_2021,
title = {Deep {Learning} for {High}-dimensional {Parameter} {Retrieval}},
isbn = {978-1-119-64618-1},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119646181.ch16},
abstract = {Modeling the state of the Earth in biology, ecology, climate, or geophysics, require describing it with a set of parameters which we can frame as bio-geophysical parameters. Retrieving these from satellites provides dense and frequent coverage and this task is an important area of Remote Sensing that supports the broader field of Earth Sciences. Parameter retrieval from satellite measurements is an area deep learning (DL) has started having a growing impact on, and this is likely to increase in the future. Despite this, several challenges need to be addressed for DL to solve and improve the current and future retrieval problems. This chapter outlines some of these challenges, which include the computational load of high-dimensional problems and defining the learning objectives for each problem at hand. Examples from two cases of bio-geophysical parameter retrieval which highlight these challenges are provided.},
language = {en},
urldate = {2021-08-28},
booktitle = {Deep learning for the {Earth} {Sciences}},
publisher = {John Wiley \& Sons, Ltd},
author = {Malmgren-Hansen, David},
year = {2021},
doi = {10.1002/9781119646181.ch16},
note = {Section: 16
\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119646181.ch16},
keywords = {bio-geophysical parameter retrieval, deep learning, earth sciences, remote sensing data},
pages = {240--257},
}
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