드론기반 시공간 초분광영상을 활용한 식생유무에 따른 하천 수심산정 기법 적용성 검토

권영화,김동수,유호준 한국수자원학회 2023 한국수자원학회논문집 Vol.56 No.4

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed. 하천법 개정 및 수자원의 조사·계획 및 관리에 관한 법률 제정으로 하상변동조사를 정기적으로 실시하는 것이 의무화되었고, 지자체가 계획적으로 수자원을 관리할 수 있도록 제도가 마련되고 있다. 하상 지형은 직접 측량할 수 없기 때문에 수심 측량을 통해 간접적으로 이루어지고 있으며, 레벨측량이나 음향측심기를 활용한 접촉식 방법으로 이루어지고 있다. 접촉식 수심측량법은 자료수집이 제한적이기 때문에 공간해상도가 낮고 연속적인 측량이 불가능하다는 한계가 있어 최근에는 LiDAR나 초분광영상을 이용한 원격탐사를 이용한 수심측정 기술이 개발되고 있다. 개발된 초분광영상을 이용한 수심측정 기술은 접촉식 조사보다 넓은 지역을 조사할 수 있고, 잦은 빈도로 자료취득이 용이한 드론에 경량 초분광센서를 탑재하여 초분광영상을 취득하고, 최적 밴드비 탐색 알고리즘을 적용해 수심분포 산정이 가능하다. 기존의 초분광 원격탐사 기법은 드론의 경로비행으로 획득한 초분광영상을 면단위의 영상으로 정합한 후 특정 물리량에 대한 분석이 수행되었으며, 수심측정의 경우 모래하천을 대상으로 한 연구가 주를 이루었으며, 하상재료에 대한 평가는 이루어지지 않았었다. 본 연구에서는 기존의 초분광영상을 활용한 수심산정 기법을 식생이 있는 하천에 적용하고, 동일지역에서 식생을 제거한 후의 2가지 케이스에 대해서 시공간 초분광영상과 단면초분광영상에 모두 적용하였다. 연구결과, 식생이 없는 경우의 수심산정이 더 높은 정확도를 보였으며, 식생이 있는 경우에는 식생의 높이를 바닥으로 인식한 수심이 산정되었다. 또한, 기존의 단면초분광영상을 이용한 수심산정뿐만 아니라 시공간 초분광영상에서도 수심산정의 높은 정확도를 보여 시공간 초분광영상을 활용한 하상변동(수심변동) 추적의 가능성을 확인하였다.

분광정합 및 혼합 분석 방법을 활용한 위험·유해물질 스티렌 탐지

박재진,박경애,김태성,이문진 해양환경안전학회 2022 해양환경안전학회지 Vol.28 No.-

As the volume of marine hazardous and noxious substances (HNSs) transported in domestic and overseas seas increases, the risk of HNS spill accidents is gradually increasing. HNS leaked into the sea causes destruction of marine ecosystems, pollution of the marine environment, and human casualties. Secondary accidents accompanied by fire and explosion are possible. Therefore, various types of HNSs must be rapidly detected, and a control strategy suitable for the characteristics of each substance must be established. In this study, the ground HNS spill experiment process and application result of detection algorithms were presented based on hyperspectral remote sensing. For this, styrene was spilled in an outdoor pool in Brest, France, and simultaneous observation was performed through a hyperspectral sensor. Pure styrene and seawater spectra were extracted by applying principal component analysis (PCA) and the N-Findr method. In addition, pixels in hyperspectral image were classified with styrene and seawater by applying spectral matching techniques such as spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), and spectral angle mapper (SAM). As a result, the SDS and SSV techniques showed good styrene detection results, and the total extent of styrene was estimated to be approximately 1.03m2. The study is expected to play a major role in marine HNS monitoring. 국내외 해상 위험·유해물질(Hazardous and Noxious Substances, HNS) 물동량이 증가함에 따라 HNS 유출 사고의 위험성이 점차 높아지고 있다. 해상에 유출된 HNS는 해양생태계 파괴를 비롯한 해양환경 오염 및 인명피해를 유발하며, 화재 및 폭발 등을 동반한 2차 사고 발생 가능성도 존재한다. 따라서 해상 HNS의 신속한 탐지와 각 물질 특성에 적합한 방제전략을 수립해야 한다. 본 연구에서는 초분광 원격탐사에 기반한 지상 HNS 유출 실험 과정 및 탐지 알고리즘 적용 결과를 제시하고자 한다. 이를 위해 프랑스 브레스트 지역의 야외 풀장에서 스티렌을 유출한 후 초분광 센서를 활용한 동시 관측을 수행하였다. 순수 스티렌 및 해수 스펙트럼은 주성분 분석(principal component analysis, PCA) 및 N-Findr 기법을 적용하여 추출하였으며, 또한 spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), spectral angle mapper (SAM)을 포함한 분광정합 기법을 적용하여 초분광 영상 내 화소들을 스티렌 및 해수로 분류하였다. 그 결과 SDS 및 SSV 기법이 우수한 스티렌 탐지 결과를 보여주었으며, 스티렌 총 면적은 약 1.03 m2로 추정되었다. 본 연구는 해상 HNS 모니터링에 주요 역할을 할 것으로 기대된다.

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

( Mohammad Raju Ahmed ),( Jannat Yasmin ),( Changyeun Mo ),( Hoonsoo Lee ),( Moon S. Kim ),( Soon-jung Hong ),( Byoung-kwan Cho ) 한국농업기계학회 2016 바이오시스템공학 Vol.41 No.4

Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

Hyperspectral Remote Sensing Image Denoising Based on Non-Local Low-Rank Dictionary Learning

Zhang Bo 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.4

For hyperspectral remote sensing image denoising, this paper proposed image denoising based on non-local low-rank dictionary learning. The basic idea of algorithm is to use strong relativity of all wave bands of hyperspectral remote sensing image with local self-similarity and local sparsity of image to improve the denoising performance. First of all, combined with the strong relativity, non-local self-similarity and local sparsity, non-local low-rank dictionary learning is established. Then iterative method is used to solve the model to get redundant dictionary and sparsity to represent coefficient. Finally, redundant dictionary and sparsity is used to express restored image of coefficient. Compared with the existing advanced algorithm, by making full use of strong relativity each band of hyperspectral image, it makes the algorithm obtain the information on details to well keep the hyperspectral remote sensing image, to improve the visual effect. Experimental results verify the effectiveness of the algorithm in this paper.

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed, Mohammad Raju,Yasmin, Jannat,Mo, Changyeun,Lee, Hoonsoo,Kim, Moon S.,Hong, Soon-Jung,Cho, Byoung-Kwan Korean Society for Agricultural Machinery 2016 바이오시스템공학 Vol.41 No.4

Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

지질자원 탐사를 위한 원격탐사 영상의 처리기법 및 활용 검토

손영선,김광은,윤왕중 한국자원공학회 2015 한국자원공학회지 Vol.52 No.4

This study was examined for the characteristics of optical sensors and applications in remote sensing for the geoscience and mineral resources. The image processing methods for mineral and ore exploration were tested in the Cuprite, Nevada. Early remote sensing techniques were used for mineral and ore exploration. More recently, remote sensing is used for interpretation of geologic structure, oil and gas exploration, environmental assessment of mining, with integrated analysis of radar image, geophysical, geochemical and geological data, using GIS. Hyperspectral image with hundreds of spectral bands can appear substantially correct spectra acquired in the field. This led to development of new image analysis techniques to obtain more accurate surface geological information. Remote sensing is likely to be an effective first step for mineral exploration in africa, central asia and polar regions where direct access to field survey is difficult for political and geographical reasons. 이 연구에서는 지질자원 원격탐사에 활용되고 있는 광학센서들의 특성과 활용사례 등을 검토하였다. 또한 광물자원 및 광상탐사를 위해 활용되고 있는 주요 영상처리 기법들을 훈련지역에 실제로 적용해보고 각기법의 특성을 고찰하였다. 초기 지질자원 원격탐사 기술은 광물 및 광상 탐사에 주로 활용되었으며, 최근에는광학영상 뿐만 아니라 레이더 영상, 지구물리, 지구화학, 지질자료 등을 GIS를 사용해 통합 분석함으로써 지질구조 해석, 석유가스 탐사, 광산개발에 따른 환경평가 등에 활용하고 있다. 수백 개의 분광밴드로 구성된 초분광영상은 실제 지상에서 획득된 분광스펙트럼과 거의 유사하게 나타낼 수 있다. 이로 인해 보다 정확한 지표지질정보를 얻기 위한 새로운 초분광 영상 분석기법들이 개발되고 있다. 원격탐사 기술은 중앙아시아, 아프리카, 극지와 같은 환경적 문제나 정치적인 문제로 현장 접근이 어려운 지역에 자원탐사에 대한 기초자료 확보에효율적인 기여를 할 수 있을 것으로 판단되며, 이를 위해 국내에서도 지질자원 원격탐사 활용을 위한 보다 다양한 연구가 필요할 것으로 보인다.

Relating Hyperspectral Image Bands and Vegetation Indices to Corn and Soybean Yield

Jang Gab-Sue,Sudduth Kenneth A.,Hong Suk-Young,Kitchen Newell R.,Palm Harlan L. The Korean Society of Remote Sensing 2006 大韓遠隔探査學會誌 Vol.22 No.3

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

지상 초분광자료를 이용한 근소만 갯벌표층에서 저서성 미세조류의 엽록소-a 공간분포 추정

고수윤 ( Sooyoon Koh ),노재훈 ( Jaehoon Noh ),백승일 ( Seungil Baek ),이호원 ( Howon Lee ),원종석 ( Jongseok Won ),김원국 ( Wonkook Kim ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.5

갯벌 표면에는 저서성 미세조류의 생체량이 높고, 그에 따라 높은 일차생산을 나타낸다. 갯벌의 탄소순환 및 유기탄소 부존량을 추산하기 위한 일차생산력 측정 연구가 기존에 진행되어 왔지만, 최근에는 광학 원격탐사, 특히 초분광센서를 이용하는 연구는 비교적 최근에 시도되기 시작하였다. 본 연구에서는 지상에서 관측된 초분광자료를 통하여 생산성 추정의 기초자료가 되는 갯벌 표면의 엽록소 농도를 추정하는 연구를 수행하였다. 연구 대상지는 충청남도 태안군에 위치한 근소만이며, 현장조사는 2021년 4월과 6월 간조시에 수행하였다. 갯벌 표면의 초분광반사도를 얻기 위하여 지향형 센서인 TriOS RAMSES와 카메라 형태의 Specim-IQ, 두 종류의 초분광센서를 사용하였고, 광학관측자료를 통해 갯벌 표면의 엽록소-a 농도를 추정하기 위해 정규 식생지수(NDVI)와 Continuum Removal Depth(CRD)기법을 사용하였다. 현장조사시 시료분석을 통해 측정한 엽록소-a 농도와의 비교 결과, 두 기법 모두 엽록소-a 농도 약 0~150 mg/㎡의 범위에 대해 추정 결정계수 약 0.7을 달성할 수 있는 것으로 나타났다. Mudflats are crucial for understanding the ecological structure and biological function of coastal ecosystem because of its high primary production by microalgae. There have been many studies on measuring primary productivity of tidal flats for the estimation of organic carbon abundance, but it is relatively recent that optical remote sensing technique, particularly hyperspectral sensing, was used for it. This study investigates hyperspectral sensing of chlorophyll concentration on a tidal flat surface, which is a key variable in deriving primary productivity. The study site is a mudflat in Geunso bay, South Korea and field campaigns were conducted at ebb tide in April and June 2021. Hyperspectral reflectance of the mudflat surfaces was measured with two types of hyperspectral sensors; TriOS RAMSES (directional sensor) and the Specim-IQ (camera sensor), and Normal Differenced Vegetation Index (NDVI) and Contiuum Removal Depth (CRD) were used to estimate Chl-a from the optical measurements. The validation performed against independent field measurements of Chl-a showed that both CRD and NDVI can retrieve surface Chl-a with R2 around 0.7 for the Chl-a range of 0~150 mg/㎡ tested in this study.

Forest Canopy Density Estimation Using Airborne Hyperspectral Data

Kwon, Tae-Hyub,Lee, Woo-Kyun,Kwak, Doo-Ahn,Park, Tae-Jin,Lee, Jong-Yoel,Hong, Suk-Young,Guishan, Cui,Kim, So-Ra The Korean Society of Remote Sensing 2012 大韓遠隔探査學會誌 Vol.28 No.3

This study was performed to estimate forest canopy density (FCD) using airborne hyperspectral data acquired in the Independence Hall of Korea in central Korea. The airborne hyperspectral data were obtained with 36 narrow spectrum ranges of visible (Red, Green, and Blue) and near infrared spectrum (NIR) scope. The FCD mapping model developed by the International Tropical Timber Organization (ITTO) uses vegetation index (VI), bare soil index (BI), shadow index (SI), and temperature index (TI) for estimating FCD. Vegetation density (VD) was calculated through the integration of VI and BI, and scaled shadow index (SSI) was extracted from SI after the detection of black soil by TI. Finally, the FCD was estimated with VD and SSI. For the estimation of FCD in this study, VI and SI were extracted from hyperspectral data. But BI and TI were not available from hyperspectral data. Hyperspectral data makes the numerous combination of each band for calculating VI and SI. Therefore, the principal component analysis (PCA) was performed to find which band combinations are explanatory. This study showed that forest canopy density can be efficiently estimated with the help of airborne hyperspectral data. Our result showed that most forest area had 60 ~ 80% canopy density. On the other hand, there was little area of 10 ~ 20% canopy density forest.

현장 및 원격 초분광 정보 계측을 통한 하천 수변공간 재료 구분

신재현,성호제,이동섭 한국수자원학회 2021 한국수자원학회논문집 Vol.54 No.12

The analysis of hyperspectral characteristics of materials near the South Han River has been conducted using riverside area measurements by drone installed hyperspectral sensors. Each spectrum reflectance of the riverside materials were compared and analyzed which were consisted of grass, concrete, soil, etc. To verify the drone installed hyperspectral measurements, a ground spectrometer was deployed for field measurements and comparisons for the materials. The comparison results showed that the riverside materials had their unique hyperspectral band characteristics, and the field measurements were similar to the remote sensing data. For the classification of the riverside area, the K-means clustering method and SVM classification method were utilized. The supervised SVM method showed accurate classification of the riverside area than the unsupervised K-means method. Using classification and clustering methods, the inherent spectral characteristic for each material was found to classify the riverside materials of hyperspectral images from drones. 본 연구에서는 남한강에서 드론에 탑재된 초분광 센서를 활용하여 수변공간을 측정한 후, 초분광 분석을 통하여 재료를 구분하였다. 식생, 콘크리트, 흙 등의 재료를 대상으로 구분하였으며, 각각 재료의 고유한 분광반사 곡선의 특성을 비교 및 분석하였다. 드론으로 측정한 초분광 자료를 검증하기 위하여 지상분광측정기를 사용하여 현장조사를 실시하고 각 재료를 비교하였다. 분석 비교 결과 각 재료별로 고유한 유형의 파장대가 발생하는 것을 확인하였고 드론으로 수행한 원격 탐사 결과가 지상분광측정 결과와 유사하다는 결론을 내릴 수 있었다. 수변 공간의 분류를 위하여 K-means 군집화 기법과 SVM 분류 기법을 활용하여 측정 구역의 공간 분류를 수행할 수 있었다. 비교 결과, 지도학습인 SVM 분류 기법의 수변공간 분류가 비지도학습인 K-means 기법과 비교하여 상세한 구분이 수행되었음을 확인할 수 있었다. 이와 같이 분류 및 군집 분석 기법을 활용하여 각 수변공간 재료의 고유 분광 특성을 활용하여 측정되는 드론탑재 초분광 이미지의 각 데이터를 분류할 수 있게 되었다.