K-sharp: A segmented regression approach for image sharpening and normalization
In recent decades, Earth Observation (EO) satellite missions have improved in spatial resolution and revisit times. These missions, traditionally government-funded, utilize state-of-the-art technology and rigorous instrument calibration, with each mission costing millions of dollars. Recently, nano-...
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ftdoajarticles:oai:doaj.org/article:7560777e68004b46a3c91cbe1546ea3d 2024-01-07T09:47:08+01:00 K-sharp: A segmented regression approach for image sharpening and normalization Bruno Aragon Kerry Cawse-Nicholson Glynn Hulley Rasmus Houborg Joshua B. Fisher 2023-12-01T00:00:00Z https://doi.org/10.1016/j.srs.2023.100095 https://doaj.org/article/7560777e68004b46a3c91cbe1546ea3d EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2666017223000202 https://doaj.org/toc/2666-0172 2666-0172 doi:10.1016/j.srs.2023.100095 https://doaj.org/article/7560777e68004b46a3c91cbe1546ea3d Science of Remote Sensing, Vol 8, Iss , Pp 100095- (2023) Image fusion CubeSat Sharpening Normalization Machine learning Physical geography GB3-5030 Science Q article 2023 ftdoajarticles https://doi.org/10.1016/j.srs.2023.100095 2023-12-10T01:38:44Z In recent decades, Earth Observation (EO) satellite missions have improved in spatial resolution and revisit times. These missions, traditionally government-funded, utilize state-of-the-art technology and rigorous instrument calibration, with each mission costing millions of dollars. Recently, nano-satellites known as CubeSats are presenting a cost-effective option for EO; their capacity of working as a constellation has brought an unprecedented opportunity for EO in terms of achievable spatial and temporal resolutions, albeit at the cost of decreased accuracy and cross-sensor consistency. As such, CubeSat datasets often require post-calibration approaches before using them for scientific applications. K-sharp is a relatively simple, data-agnostic machine learning approach that combines K-means and partial least squares regression to derive relationships between two sets of images for normalization. This study used Planet's four-band CubeSat imagery to sharpen day-coincident Landsat 8 normalized difference vegetation index, albedo, and the first short-wave infrared (SWIR) band from 30 m to 3 m spatial resolution (it should be noted that the four-band CubeSat product does not include the first SWIR band, and that the calculation of albedo is not directly possible from this product). K-sharp was tested over agricultural, savanna, rainforest, and tundra sites with and without atmospheric correction. Our model reproduced surface conditions with an average r2 of 0.88 (rMAE = 11.39%) across all study sites and target variables when compared against the original Landsat 8 data. These results showcase the promising potential of K-sharp in generating precise, CubeSat-derived datasets with high radiometric quality, which can be incorporated into agricultural or ecological applications to enhance their decision-making process at fine spatial scales. Article in Journal/Newspaper Tundra Directory of Open Access Journals: DOAJ Articles Science of Remote Sensing 8 100095 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Image fusion CubeSat Sharpening Normalization Machine learning Physical geography GB3-5030 Science Q |
spellingShingle |
Image fusion CubeSat Sharpening Normalization Machine learning Physical geography GB3-5030 Science Q Bruno Aragon Kerry Cawse-Nicholson Glynn Hulley Rasmus Houborg Joshua B. Fisher K-sharp: A segmented regression approach for image sharpening and normalization |
topic_facet |
Image fusion CubeSat Sharpening Normalization Machine learning Physical geography GB3-5030 Science Q |
description |
In recent decades, Earth Observation (EO) satellite missions have improved in spatial resolution and revisit times. These missions, traditionally government-funded, utilize state-of-the-art technology and rigorous instrument calibration, with each mission costing millions of dollars. Recently, nano-satellites known as CubeSats are presenting a cost-effective option for EO; their capacity of working as a constellation has brought an unprecedented opportunity for EO in terms of achievable spatial and temporal resolutions, albeit at the cost of decreased accuracy and cross-sensor consistency. As such, CubeSat datasets often require post-calibration approaches before using them for scientific applications. K-sharp is a relatively simple, data-agnostic machine learning approach that combines K-means and partial least squares regression to derive relationships between two sets of images for normalization. This study used Planet's four-band CubeSat imagery to sharpen day-coincident Landsat 8 normalized difference vegetation index, albedo, and the first short-wave infrared (SWIR) band from 30 m to 3 m spatial resolution (it should be noted that the four-band CubeSat product does not include the first SWIR band, and that the calculation of albedo is not directly possible from this product). K-sharp was tested over agricultural, savanna, rainforest, and tundra sites with and without atmospheric correction. Our model reproduced surface conditions with an average r2 of 0.88 (rMAE = 11.39%) across all study sites and target variables when compared against the original Landsat 8 data. These results showcase the promising potential of K-sharp in generating precise, CubeSat-derived datasets with high radiometric quality, which can be incorporated into agricultural or ecological applications to enhance their decision-making process at fine spatial scales. |
format |
Article in Journal/Newspaper |
author |
Bruno Aragon Kerry Cawse-Nicholson Glynn Hulley Rasmus Houborg Joshua B. Fisher |
author_facet |
Bruno Aragon Kerry Cawse-Nicholson Glynn Hulley Rasmus Houborg Joshua B. Fisher |
author_sort |
Bruno Aragon |
title |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_short |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_full |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_fullStr |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_full_unstemmed |
K-sharp: A segmented regression approach for image sharpening and normalization |
title_sort |
k-sharp: a segmented regression approach for image sharpening and normalization |
publisher |
Elsevier |
publishDate |
2023 |
url |
https://doi.org/10.1016/j.srs.2023.100095 https://doaj.org/article/7560777e68004b46a3c91cbe1546ea3d |
genre |
Tundra |
genre_facet |
Tundra |
op_source |
Science of Remote Sensing, Vol 8, Iss , Pp 100095- (2023) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2666017223000202 https://doaj.org/toc/2666-0172 2666-0172 doi:10.1016/j.srs.2023.100095 https://doaj.org/article/7560777e68004b46a3c91cbe1546ea3d |
op_doi |
https://doi.org/10.1016/j.srs.2023.100095 |
container_title |
Science of Remote Sensing |
container_volume |
8 |
container_start_page |
100095 |
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