• Journal of Biosystems Engineering
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• 제41권4호
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• pp.396-407
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• 2016

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.

• 한국군사과학기술학회지
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• 제13권2호
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• pp.328-335
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• 2010

A hyperspectral imaging spectrometer has shown significant advantages in performance over other existing ones for remote sensing applications. It can collect hundreds of narrow, adjacent spectral bands for each image, which provides a wealth of information on unique spectral characteristics of objects. We have developed a compact hyperspectral imaging system that successively shows high spatial and spectral resolutions and fast data processing performance. In this paper, we present an overview of the hyperspectral imaging system including the strucure of geometrical optics and several image processing schemes such as wavelength calibration and noise reduction for image data on Visible and Near-Infrared(VNIR) and Shortwave-Infrared(SWIR) band.

• 한국광학회지
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• 제32권5호
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• pp.215-219
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• 2021

최근 농작물의 상황을 실시간이면서도 비파괴적으로 측정하는 시스템이 스마트팜 등의 분야에서 필수적인 요소로 주목받고 있다. 본 연구에서는 초분광 영상 기술을 통해 많은 개체 수의 청경채 유묘 내의 안토시아닌 함량을 비파괴적으로 동시에 측정하였다. 많은 유묘의 동시 측정을 위해서, 기존의 초분광 영상 시스템의 실험 구성을 수정하였다. 품종당 24개씩 총 96개의 유묘를 측정하였고, 한번의 초분광 데이터 획득시 12개의 유묘가 동시에 분석 가능했으며, 총 3분이 소요된다. 본 논문에서 제안한 초분광 영상 기술은 파괴적 화학 분석 방법과 비교 가능한 분석 시스템을 제공하는 것으로 나타났다. 또한 많은 수의 식물을 동시에 측정함으로써, 초분광 영상 기술이 초고속 피노타이핑 시스템에 적용될 수 있다는 가능성을 확인하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.213-216
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• 2005

A novel hyperspectral imaging spectrometer controlling spatial and spectral resolution individually has been proposed. This imaging spectrometer uses a zoom lens as a telescope and a focusing element. It can change the spatial resolution fixing the spectral resolution or the spectral resolution fixing the spatial resolution. Here, we report the concept of the hyperspectral imaging spectrometer with the novel zooming function and the optical design of a zoom lens as the focusing element. By using lens module and third-order aberration theory, we have presented the initial design of four-group zoom lens with external entrance pupil. And the optimized zoom lens with a focal length of 50 to 150 mm is obtained from the initial design by the optical design software. As a result, the designed zoom lens shows satisfactory performances in wavelength range of 450 to 900 nm as a focusing element in an imaging spectrometer. Furthermore, the collimator lens of the imaging spectrometer is designed through the third-order aberration correction by using an iterative process.

• 한국식품저장유통학회지
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• 제30권2호
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• pp.224-234
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• 2023

We analyzed the major quality characteristics of red pepper powders from various regions and predicted these characteristics nondestructively using shortwave infrared hyperspectral imaging (HSI) technology. We conducted partial least squares regression analysis on 70% (n=71) of the acquired hyperspectral data of the red pepper powders to examine the major quality characteristics. R_c² values of ≥0.8 were obtained for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The developed quality prediction model was validated using the remaining 30% (n=35) of the hyperspectral data; the highest accuracy was achieved for the ASTA color value (R_p²=0.8488), and similar validity levels were achieved for the capsaicinoid and moisture contents. To increase the accuracy of the quality prediction model, we conducted spectrum preprocessing using SNV, MSC, SG-1, and SG-2, and the model's accuracy was verified. The results indicated that the accuracy of the model was most significantly improved by the MSC method, and the prediction accuracy for the ASTA color value was the highest for all the spectrum preprocessing methods. Our findings suggest that the quality characteristics of red pepper powders, even powders that do not conform to specific variables such as particle size and moisture content, can be predicted via HSI.

• Structural Monitoring and Maintenance
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• 제2권3호
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• pp.181-197
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• 2015

Assessing the condition of paint on civil structures is an important but challenging and costly task, in particular when it comes to large and complex structures. Current practices of visual inspection are labour-intensive and time-consuming to perform. In addition, this task usually relies on the experience and subjective judgment of individual inspectors. In this study, hyperspectral imaging and classification techniques are proposed as a method to objectively assess the state of the paint on a civil or other structure. The ultimate objective of the work is to develop a technology that can provide precise and automatic grading of paint condition and assessment of degradation due to age or environmental factors. Towards this goal, we acquired hyperspectral images of steel surfaces located at long (mid-range) and short distances on the Sydney Harbour Bridge with an Acousto-Optics Tunable filter (AOTF) hyperspectral camera (consisting of 21 bands in the visible spectrum). We trained a multi-class Support Vector Machines (SVM) classifier to automatically assess the grading of the paint from hyperspectral signatures. Our results demonstrate that the classifier generates highly accurate assessment of the paint condition in comparison to the judgement of human experts.

• 농업과학연구
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• 제47권3호
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• pp.633-644
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• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3338-3355
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• 2018

Images acquired by Large Aperture Static Imaging Spectrometer (LASIS) exhibit obvious interference stripes, which are vertical and stationary due to the special imaging principle of interference hyperspectral image (IHI) data. As the special characteristics above will seriously affect the intrinsic structure and sparsity of IHI, decomposition of IHI has drawn considerable attentions of many scientists and lots of efforts have been made. Although some decomposition methods for interference hyperspectral data have been proposed to solve the above problem of interference stripes, too many times of iteration are necessary to get an optimal solution, which will severely affect the efficiency of application. A novel algorithm for decomposition of interference hyperspectral images based on split Bregman iteration is proposed in this paper, compared with other decomposition methods, numerical experiments have proved that the proposed method will be much more efficient and can reduce the times of iteration significantly.

• 한국측량학회지
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• 제32권spc4_2호
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• pp.421-430
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• 2014

항공영상, 위성영상 및 초분광영상은 농업, 산림, 수계 해안, 지질, 토지피복 지도 작성 등에 널리 이용되고 있지만, 지적분야에서 이들의 활용은 거의 나타나지 않고 있다. 한편 해외에서는 항공 위성영상의 지적도와 중첩이나 지목의 등록 및 갱신과 관련된 연구 사례들이 보고되고 있다. 이에 본 연구에서는 초분광영상을 지적 분야 적용성 검토결과, 기존 지목 오류 조사를 위한 현장 공간정보 취득 수단으로 활용될 수 있을 것으로 판단되며 향후 다목적 지적 구현 시 지적정보와 융합할 수 있는 농업, 토양, 그리고 식생 등의 속성정보 취득에 기여할 것으로 사료된다.

• 한국수자원학회논문집
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• 제56권4호
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• pp.235-243
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• 2023

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.