Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements

Empirical line methods (ELM) are frequently used to correct images from aerial remote sensing. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The small signal response of the water is proportionally smaller when compared to the other land surface targets.

 

This dataset presents some resources and results of a new approach to calibrate empirical lines combining reference calibration panels with water samples. We optimize the method using python algorithms until reaches the best result.

 

Dataset Files

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[1] Alisson Carmo, Nariane Bernardo, Nilton Imai, Milton Shimabukuro, "Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/fbks-rj40. Accessed: Jul. 07, 2020.
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doi = {10.21227/fbks-rj40},
url = {http://dx.doi.org/10.21227/fbks-rj40},
author = {Alisson Carmo; Nariane Bernardo; Nilton Imai; Milton Shimabukuro },
publisher = {IEEE Dataport},
title = {Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements},
year = {2019} }
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T1 - Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements
AU - Alisson Carmo; Nariane Bernardo; Nilton Imai; Milton Shimabukuro
PY - 2019
PB - IEEE Dataport
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Alisson Carmo, Nariane Bernardo, Nilton Imai, Milton Shimabukuro. (2019). Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements. IEEE Dataport. http://dx.doi.org/10.21227/fbks-rj40
Alisson Carmo, Nariane Bernardo, Nilton Imai, Milton Shimabukuro, 2019. Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements. Available at: http://dx.doi.org/10.21227/fbks-rj40.
Alisson Carmo, Nariane Bernardo, Nilton Imai, Milton Shimabukuro. (2019). "Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements." Web.
1. Alisson Carmo, Nariane Bernardo, Nilton Imai, Milton Shimabukuro. Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/fbks-rj40
Alisson Carmo, Nariane Bernardo, Nilton Imai, Milton Shimabukuro. "Python algorithms and dataset of empirical line method applied to inland water hyperspectral images combining reference targets and in situ water measurements." doi: 10.21227/fbks-rj40