High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems
Abstract Hyperspectral data are gaining popularity in remote sensing and signal processing communities because of the increased spectral information relative to multispectral data. Several airborne and spaceborne hyperspectral datasets are publicly available, facilitating the development of various...
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Online Access: | https://doi.org/10.1002/gdj3.133 https://doaj.org/article/cee4c7e1d0c94bba97c827a79d29bda7 |
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ftdoajarticles:oai:doaj.org/article:cee4c7e1d0c94bba97c827a79d29bda7 2023-05-15T17:57:47+02:00 High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems Jae‐In Kim Junhwa Chi Ali Masjedi John Evan Flatt Melba M. Crawford Ayman F. Habib Joohan Lee Hyun‐Cheol Kim 2022-11-01T00:00:00Z https://doi.org/10.1002/gdj3.133 https://doaj.org/article/cee4c7e1d0c94bba97c827a79d29bda7 EN eng Wiley https://doi.org/10.1002/gdj3.133 https://doaj.org/toc/2049-6060 2049-6060 doi:10.1002/gdj3.133 https://doaj.org/article/cee4c7e1d0c94bba97c827a79d29bda7 Geoscience Data Journal, Vol 9, Iss 2, Pp 221-234 (2022) geometric correction hyperspectral permafrost radiometric correction unmanned aerial vehicle Meteorology. Climatology QC851-999 Geology QE1-996.5 article 2022 ftdoajarticles https://doi.org/10.1002/gdj3.133 2022-12-30T21:09:09Z Abstract Hyperspectral data are gaining popularity in remote sensing and signal processing communities because of the increased spectral information relative to multispectral data. Several airborne and spaceborne hyperspectral datasets are publicly available, facilitating the development of various applications and algorithms. However, hyperspectral data are usually limited by their narrow, highly correlated and contiguous spectral bands in both processing and analysis. Moreover, the resolution of available hyperspectral datasets is not sufficiently high for the identification of small objects. Nevertheless, with the rapidly advancing technology, hyperspectral imaging systems can now be mounted on small aerial vehicles for detecting small objects at low altitude. To properly handle these high spectral and spatial resolution data, new or redesigned data processing or analysis pipelines must be developed, but such datasets are limited. In this study, we describe two hyperspectral datasets acquired by a drone and evaluate their radiometric and geometric quality. Based on appropriate data acquisition and processing approaches, our datasets are expected to be useful as testbeds for new algorithms and applications. Article in Journal/Newspaper permafrost Directory of Open Access Journals: DOAJ Articles Geoscience Data Journal 9 2 221 234 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
geometric correction hyperspectral permafrost radiometric correction unmanned aerial vehicle Meteorology. Climatology QC851-999 Geology QE1-996.5 |
spellingShingle |
geometric correction hyperspectral permafrost radiometric correction unmanned aerial vehicle Meteorology. Climatology QC851-999 Geology QE1-996.5 Jae‐In Kim Junhwa Chi Ali Masjedi John Evan Flatt Melba M. Crawford Ayman F. Habib Joohan Lee Hyun‐Cheol Kim High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems |
topic_facet |
geometric correction hyperspectral permafrost radiometric correction unmanned aerial vehicle Meteorology. Climatology QC851-999 Geology QE1-996.5 |
description |
Abstract Hyperspectral data are gaining popularity in remote sensing and signal processing communities because of the increased spectral information relative to multispectral data. Several airborne and spaceborne hyperspectral datasets are publicly available, facilitating the development of various applications and algorithms. However, hyperspectral data are usually limited by their narrow, highly correlated and contiguous spectral bands in both processing and analysis. Moreover, the resolution of available hyperspectral datasets is not sufficiently high for the identification of small objects. Nevertheless, with the rapidly advancing technology, hyperspectral imaging systems can now be mounted on small aerial vehicles for detecting small objects at low altitude. To properly handle these high spectral and spatial resolution data, new or redesigned data processing or analysis pipelines must be developed, but such datasets are limited. In this study, we describe two hyperspectral datasets acquired by a drone and evaluate their radiometric and geometric quality. Based on appropriate data acquisition and processing approaches, our datasets are expected to be useful as testbeds for new algorithms and applications. |
format |
Article in Journal/Newspaper |
author |
Jae‐In Kim Junhwa Chi Ali Masjedi John Evan Flatt Melba M. Crawford Ayman F. Habib Joohan Lee Hyun‐Cheol Kim |
author_facet |
Jae‐In Kim Junhwa Chi Ali Masjedi John Evan Flatt Melba M. Crawford Ayman F. Habib Joohan Lee Hyun‐Cheol Kim |
author_sort |
Jae‐In Kim |
title |
High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems |
title_short |
High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems |
title_full |
High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems |
title_fullStr |
High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems |
title_full_unstemmed |
High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems |
title_sort |
high‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems |
publisher |
Wiley |
publishDate |
2022 |
url |
https://doi.org/10.1002/gdj3.133 https://doaj.org/article/cee4c7e1d0c94bba97c827a79d29bda7 |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Geoscience Data Journal, Vol 9, Iss 2, Pp 221-234 (2022) |
op_relation |
https://doi.org/10.1002/gdj3.133 https://doaj.org/toc/2049-6060 2049-6060 doi:10.1002/gdj3.133 https://doaj.org/article/cee4c7e1d0c94bba97c827a79d29bda7 |
op_doi |
https://doi.org/10.1002/gdj3.133 |
container_title |
Geoscience Data Journal |
container_volume |
9 |
container_issue |
2 |
container_start_page |
221 |
op_container_end_page |
234 |
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1766166289543331840 |