An accurate monitoring method of peanut southern blight using unmanned aerial vehicle remote sensing
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A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The overall model incorporates four components; a mission flight model, a multispectral target and background signature model, a multispectral sensor model, and a multispectral target detection model. Emphasis is placed on estimating the effects of mission multispectral target detection algorithms. Thus, the model ideally supports mission and multispectral sensor trade studies which require optimization of the system's overall target detection performance. The model and a typical example of performance prediction results are presented.
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온주밀감은 한국에서 가장 많이 소비되는 감귤품종으로, 높은 수요를 충족시키기 위하여 생산량을 적절히 관리해야 한다. 그러나 현행되는 재배기술은 스트레스에 노출된 작물에 대해 정시 처방을 제공하기 힘든 수작업에 의존하고 있다. UAV (Unmanned Aerial Vehicle) 를 활용한 원격 탐사 기술은 이에 대한 효과적인 대안이 될 수 있다. 높은 해상도로 세밀한 관측이 가능한 저고도 UAV 기반 원격 탐사 기술은 정밀농업 분야에 유용한 정보를 제공할 수 있다. Hyperspectral / Multispectral 이미징 기술은 대상의 물리화학적 특성을 측정하는 것에 신뢰할만한 성능을 보였다. 특히 소수의 파장만을 이용하는 Multispectral 이미징은 Hyperspectral 이미징에 비해 분석 속도가 빠르고 비용이 저렴하다는 장점이 있다. 이에 본 연구에서는 UAV 및 Multispectral imager 로 구성된 원격 탐사 시스템을 이용하여 온주밀감 나무의 비 생물적 스트레스를 모니터링 하고자 하였다. 정상 및 수분, 질소 스트레스에 노출된 작물에 대한 Multispectral Vis/NIR 이미지를 획득하였다. 이후 획득된 이미지에 대한 정사 영상을 추출하고, 광조건 및 대기조건 변화에 의한 오차를 최소화하고자 방사보정을 적용하였다. 온주밀감 나무의 스트레스 증상을 측정하기 위해 다양한 전처리와 함께 부분최소제곱 판별분석(PLS-DA) 및 NDVI 식생지수 분석을 수행한 결과, 스트레스에 노출된 감귤나무의 분류에 대해 합리적인 정확도의 결과를 얻을 수 있었다. 이는 Multispectralimager 및 UAV 를 이용한 원격 탐사 시스템이 온주밀감 나무의 신속하고 비파괴적인 스트레스 모니터링에 좋은 가능성을 가지고 있음을 보여주었다.
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In this work, destabilizing factors affecting the quality and resolution of images, multispectral monitoring systems in the optical (visible and infrared) and microwave bands are analyzed and systematized. The analysis of the influence of destabilizing factors on the formation of images in multispectral monitoring systems allows us to take into account the described effects when creating of domestic highly effective multispectral monitoring systems.
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We present a two-dimensional (2D) snapshot multispectral imager that utilizes the optical transmission characteristics of nanohole arrays (NHAs) in a gold film to resolve a mixture of input colors into multiple spectral bands. The multispectral device consists of blocks of NHAs, wherein each NHA has a unique periodicity that results in transmission resonances and minima in the visible and near-infrared regions. The multispectral device was illuminated over a wide spectral range and the transmission was spectrally unmixed using a least-squares estimation algorithm. A NHA-based multispectral imaging system was built and tested in both reflection and transmission modes. The NHA-based multispectral imager was capable of extracting 2D multispectral images representative of four independent bands within the spectral range of 662 nm to 832 nm for a variety of targets. The multispectral device can potentially be integrated into a variety of imaging sensor systems.
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Currently, airborne multispectral cameras, represented by developed countries such as the United States and Israel, have been applied. Airborne multispectral detection technology is affected by long-distance and strong interference, and the obtained multispectral images are different from existing near-field research results in feature recognition, which poses direct application difficulties. This paper studies the limitations of airborne multispectral imaging detection and relevant multispectral image processing and reconstruction algorithms, then introduce a device to get target/background spectral characteristics, and finally the target material feature recognition method.
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Multispectral constraints are exploited for optical flow computation. The theoretical basis and conditions for using multispectral images are described. An optical flow algorithm using multispectral constraints is outlined. Tests of the algorithm on real image sequences show that various multispectral constraints from the visible and infrared spectrum can be used to compute optical flow fields in the presence of noise.< >
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Multispectral imaging has been proposed as a solution to most of the problems of conventional image reproduction. The paper is focused on the practical problems of designing and operating a multispectral scanner. Several design parameters of a multispectral imaging device are discussed, including the positioning of the multispectral filters and the spectral reconstruction algorithm used during the image capture process. Finally, experimental results are given.
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Thanks to some technical progress in interferencefilter design based on different technologies, we can finally successfully implement the concept of multispectral filter array-based sensors. This article provides the relevant state-of-the-art for multispectral imaging systems and presents the characteristics of the elements of our multispectral sensor as a case study. The spectral characteristics are based on two different spatial arrangements that distribute eight different bandpass filters in the visible and near-infrared area of the spectrum. We demonstrate that the system is viable and evaluate its performance through sensor spectral simulation.
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This chapter contains sections titled: What is a Multispectral Image? Multispectral Image Acquisition Fusion of Visible and Infrared Images for Face Recognition Multispectral Image Fusion in the Visible Spectrum for Face Recognition References
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The use of multispectral images has great potential to assess seed quality and represents a significant technological advance in the search for fast and non-destructive analysis techniques. However, the devices currently available are expensive. Thus, this study aimed to propose a low-cost method for acquisition and processing of multispectral images of soybean seeds and to evaluate their potential for rapid determination of seed physiological potential. The study was conducted in three steps: implementation of the multispectral image acquisition system, development of an algorithm for automatic image processing, and evaluation of the relationship between the data obtained through image analysis and the results of standard tests used to evaluate seed physiological potential. A total of 43 variables were assessed, eight related to seed physiological potential (germination and vigor) and 35 obtained from the analysis of the multispectral images. Of the variables obtained from multispectral images, 21 were related to pixel values in the images in the different bands evaluated (green, red, and infrared) and 14 associated with seed morphometric characteristics. The proposed system is efficient in obtaining multispectral images and the algorithm developed was efficient to extract morphometric characteristics and pixel information from the images. The parameters obtained from the NIR spectrum region showed a good relationship with the physiological potential of soybean seeds.
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