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...

Full description

Bibliographic Details
Main Authors: Wonkook Kim, Seungil Baek, Soon Heun Hong
Format: Article
Language:English
Published: GeoAI Data Society 2023-12-01
Series:Geo Data
Subjects:
Online Access:http://geodata.kr/upload/pdf/GD-2023-0035.pdf
_version_ 1797306977329610752
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