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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-11-01
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| Series: | Geoscience Data Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/gdj3.133 |
| _version_ | 1811212258605268992 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T05:26:04Z |
| format | Article |
| id | doaj.art-cee4c7e1d0c94bba97c827a79d29bda7 |
| institution | Directory Open Access Journal |
| issn | 2049-6060 |
| language | English |
| last_indexed | 2024-04-12T05:26:04Z |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geoscience Data Journal |
| spelling | doaj.art-cee4c7e1d0c94bba97c827a79d29bda72022-12-22T03:46:18ZengWileyGeoscience Data Journal2049-60602022-11-019222123410.1002/gdj3.133High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systemsJae‐In Kim0Junhwa Chi1Ali Masjedi2John Evan Flatt3Melba M. Crawford4Ayman F. Habib5Joohan Lee6Hyun‐Cheol Kim7Korea Polar Research Institute Incheon KoreaKorea Polar Research Institute Incheon KoreaPurdue University West Lafayette IN USAGRYFN West Lafayette IN USAPurdue University West Lafayette IN USAPurdue University West Lafayette IN USAKorea Polar Research Institute Incheon KoreaKorea Polar Research Institute Incheon KoreaAbstract 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.https://doi.org/10.1002/gdj3.133geometric correctionhyperspectralpermafrostradiometric correctionunmanned aerial vehicle |
| spellingShingle | 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 Geoscience Data Journal geometric correction hyperspectral permafrost radiometric correction unmanned aerial vehicle |
| title | 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_short | High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems |
| title_sort | high resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems |
| topic | geometric correction hyperspectral permafrost radiometric correction unmanned aerial vehicle |
| url | https://doi.org/10.1002/gdj3.133 |
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