De-Striping and De-Smiling of Aerial Hyperspectral Image for Water Color Analysis
Hyperspectral imagery is typically acquired in push-broom mechanism, which is prone to image artifacts such as striping and smile for the sensor array whose sensor elements are not perfectly calibrated. The best practice would be to calibrate the sensor elements before the flight, but post-correctio...
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Format: | Article |
Language: | English |
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GeoAI Data Society
2023-12-01
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Series: | Geo Data |
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Online Access: | http://geodata.kr/upload/pdf/GD-2023-0035.pdf |
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author | Wonkook Kim Seungil Baek Soon Heun Hong |
author_facet | Wonkook Kim Seungil Baek Soon Heun Hong |
author_sort | Wonkook Kim |
collection | DOAJ |
description | Hyperspectral imagery is typically acquired in push-broom mechanism, which is prone to image artifacts such as striping and smile for the sensor array whose sensor elements are not perfectly calibrated. The best practice would be to calibrate the sensor elements before the flight, but post-correction is required when images are already acquired without calibration. While there are some studies that addressed those striping and smile effects for hyperspectral images acquired from satellite or aircraft platforms, few studies were done for hyperspectral images that are focusing water color analysis, where the radiance level is approximately only a tenth of terrestrial scenes. This study proposes a correction method specialized for water scenes that also may contain terrestrial objects together, and analyzes the results using real drone-borne hyperspectral imagery taken for an island area in Korea. The result revealed that the variation in columnar mean before the de-striping, which ranges 5-15%, reduced to under 2% after the correction, also exhibiting successful removal of striping in visual inspection. The smile effect that ranges approximately 1-2 mW/m2/nm/sr, which accounts for 30% of radiance from water area, also reduced to under 0.1 mW/m2/nm/sr after the smile correction. |
first_indexed | 2024-03-08T00:50:39Z |
format | Article |
id | doaj.art-1dd7fec685404004a6b93a9eb7c1bb58 |
institution | Directory Open Access Journal |
issn | 2713-5004 |
language | English |
last_indexed | 2024-03-08T00:50:39Z |
publishDate | 2023-12-01 |
publisher | GeoAI Data Society |
record_format | Article |
series | Geo Data |
spelling | doaj.art-1dd7fec685404004a6b93a9eb7c1bb582024-02-15T04:56:10ZengGeoAI Data SocietyGeo Data2713-50042023-12-015435536310.22761/GD.2023.0035107De-Striping and De-Smiling of Aerial Hyperspectral Image for Water Color AnalysisWonkook Kim0Seungil Baek1Soon Heun Hong2Associate Professor, Department of Enviromental Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, 46241 Busan, South KoreaPh.D Student, Department of Enviromental Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, 46241 Busan, South KoreaProfessor, Department of Enviromental Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, 46241 Busan, South KoreaHyperspectral imagery is typically acquired in push-broom mechanism, which is prone to image artifacts such as striping and smile for the sensor array whose sensor elements are not perfectly calibrated. The best practice would be to calibrate the sensor elements before the flight, but post-correction is required when images are already acquired without calibration. While there are some studies that addressed those striping and smile effects for hyperspectral images acquired from satellite or aircraft platforms, few studies were done for hyperspectral images that are focusing water color analysis, where the radiance level is approximately only a tenth of terrestrial scenes. This study proposes a correction method specialized for water scenes that also may contain terrestrial objects together, and analyzes the results using real drone-borne hyperspectral imagery taken for an island area in Korea. The result revealed that the variation in columnar mean before the de-striping, which ranges 5-15%, reduced to under 2% after the correction, also exhibiting successful removal of striping in visual inspection. The smile effect that ranges approximately 1-2 mW/m2/nm/sr, which accounts for 30% of radiance from water area, also reduced to under 0.1 mW/m2/nm/sr after the smile correction.http://geodata.kr/upload/pdf/GD-2023-0035.pdfstripingsmilehyperspectralpush broom coastal water |
spellingShingle | Wonkook Kim Seungil Baek Soon Heun Hong De-Striping and De-Smiling of Aerial Hyperspectral Image for Water Color Analysis Geo Data striping smile hyperspectral push broom coastal water |
title | De-Striping and De-Smiling of Aerial Hyperspectral Image for Water Color Analysis |
title_full | De-Striping and De-Smiling of Aerial Hyperspectral Image for Water Color Analysis |
title_fullStr | De-Striping and De-Smiling of Aerial Hyperspectral Image for Water Color Analysis |
title_full_unstemmed | De-Striping and De-Smiling of Aerial Hyperspectral Image for Water Color Analysis |
title_short | De-Striping and De-Smiling of Aerial Hyperspectral Image for Water Color Analysis |
title_sort | de striping and de smiling of aerial hyperspectral image for water color analysis |
topic | striping smile hyperspectral push broom coastal water |
url | http://geodata.kr/upload/pdf/GD-2023-0035.pdf |
work_keys_str_mv | AT wonkookkim destripinganddesmilingofaerialhyperspectralimageforwatercoloranalysis AT seungilbaek destripinganddesmilingofaerialhyperspectralimageforwatercoloranalysis AT soonheunhong destripinganddesmilingofaerialhyperspectralimageforwatercoloranalysis |