Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm
Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors...
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doaj-0cb4b5ca1aa847b1945325ed664e4c192021-03-31T23:03:30ZengMDPI AGSensors1424-82202021-03-01212407240710.3390/s21072407Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow AlgorithmHojun You0Dongsu Kim1IIHR—Hydroscience and Engineering, University of Iowa, Iowa City, IA 52242, USADepartment of Civil and Environmental Engineering, Dankook University, Gyeonggi-do 16890, KoreaFluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.https://www.mdpi.com/1424-8220/21/7/2407fluvial remote sensinghyperspectral imageryoptical flowimage registration |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hojun You Dongsu Kim |
spellingShingle |
Hojun You Dongsu Kim Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm Sensors fluvial remote sensing hyperspectral imagery optical flow image registration |
author_facet |
Hojun You Dongsu Kim |
author_sort |
Hojun You |
title |
Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm |
title_short |
Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm |
title_full |
Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm |
title_fullStr |
Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm |
title_full_unstemmed |
Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm |
title_sort |
development of an image registration technique for fluvial hyperspectral imagery using an optical flow algorithm |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-03-01 |
description |
Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching. |
topic |
fluvial remote sensing hyperspectral imagery optical flow image registration |
url |
https://www.mdpi.com/1424-8220/21/7/2407 |
work_keys_str_mv |
AT hojunyou developmentofanimageregistrationtechniqueforfluvialhyperspectralimageryusinganopticalflowalgorithm AT dongsukim developmentofanimageregistrationtechniqueforfluvialhyperspectralimageryusinganopticalflowalgorithm |
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